{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "John Leung",
  "title_listed": "Managing the Technical Debts of Machine Learning Systems",
  "keywords": "",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/managing-the-technical-debts-of-machine-learning-systems-5b85d420ab9d/",
  "publish_date": "2023-09-26",
  "corpus_stage": "",
  "notes": "",
  "id": "b5a28259ab43",
  "collected_at": "2026-10-02T04:34:00",
  "title": "Managing the Technical Debts of Machine Learning Systems | Towards Data Science",
  "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Managing the Technical Debts of Machine Learning Systems\nExplore the practices with implementation codes for sustainably mitigating the cost of speedy delivery\n[John Leung](https://towardsdatascience.com/author/johnleungtj/)\nSeptember 26, 202310 min read\n# Managing the Technical Debt of Machine Learning Systems\n### Explore the practices (design patterns, version control, and monitoring systems) for sustainably mitigating the cost of speedy delivery—with implementation codes\nAs the machine learning (ML) community advances over the years, the resources available for developing ML projects are plentiful. For example, we can rely on the generic Python package [scikit-learn](https://scikit-learn.org/stable/), which is built on NumPy, SciPy, and matplotlib, for data preprocessing and basic predictive tasks. Or we can leverage the open-source collection of [pre-trained models](https://huggingface.co/models?sort=downloads) from Hugging Face for analyzing diverse types of datasets. These empower current data scientists to quickly and effortlessly tackle standard ML tasks while achieving moderately good model performance.\nHowever, the abundance of ML tools often leads business stakeholders and even practitioners to underestimate the effort required to build enterprise-level ML systems. Particularly when faced with tight project deadlines, the teams may expedite deploying systems to production without giving sufficient technical considerations. Consequently, the ML system often does not address the business needs in a technically sustainable and maintainable manner.\n> As the system evolves and deploys over time, technical debts accumulate - The longer the implied cost remains unaddressed, the more costly it becomes to rectify them.\n![Photo by Andrea De Santis on Unsplash](../images/000.jpg)\nPhoto by [Andrea De Santis](https://unsplash.com/@santesson89?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nThere are multiple sources of technical debts in the ML system. Some are included below.\n### 1 Inflexible code design to cater to unforeseen requirements\nTo validate if ML can address the enterprise challenges at hand, many ML projects commence with a [proof of concept (PoC)](https://en.wikipedia.org/wiki/Proof_of_concept). We initially created a Jupyter Notebook or Google Colab environment to explore data, then developed several ad-hoc functions, and created the illusion of nearing project completion for stakeholders. **Such systems building directly from PoC may end up consisting mostly of**[**glue code**](https://en.wikipedia.org/wiki/Glue_code) - the supporting code that connects specific incompatible components but itself does not have the functionality of data analysis. They can be spaghetti-like, hard to maintain, and prone to errors.\n![Photo by Jakob Owens on Unsplash](../images/001.jpg)\nPhoto by [Jakob Owens](https://unsplash.com/@jakobowens1?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nBusiness stakeholders often present alternate requirements from time to time or wish to scale up the project, such as trying out new data sources or new algorithms. We thus often find ourselves frequently revisiting the codebase covering the current preprocessing pipelines and model development processes. Inflexible code design can consequently lead to difficulties in reacting to new changes, or rewrite most of the codes for even minor adjustments.\n### 2 Messiness in the configurations of the ML system\nSoftware engineering programming automates tasks by defining rules for computers to follow, ensuring precise output for the same input. Software engineers are also concerned with the correctness of every corner case. On the other hand, ML systems automate tasks by collecting and feeding feature data into models to achieve desired target results. This experimental process embraces uncertainty and variability. **As ML systems mature, they often contain multiple versions of configuration options** , such as datasets with different feature combinations, and algorithm-specific learning settings.\n![Photo by Ricardo Viana on Unsplash](../images/002.jpg)\nPhoto by [Ricardo Viana](https://unsplash.com/@ricardoviana?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nInput features in ML systems are inherently interconnected. Consider a scenario where feature A is no longer available as an input for your ML systems in production, you are required to re-evaluate the weighting of the remaining features for production serving. However, after 2 months, feature A becomes available again. If you did not systematically save or even mistakenly modify the original configurations, rectifying the decrease in performance would require additional computational resources and time effort.\n### 3 Limited ability to adapt to the evolving external world\nML systems often have dependencies on the external world, and various hidden factors continually evolve but are not being appropriately considered and monitored.\n![External factors leading to potential degradation of model performance \\(image by author\\)](../images/003.png)\nExternal factors leading to potential degradation of model performance (image by author)\n  * **Unstable data output from upstream producers** : The input signal of our ML system may come from another machine learning model that updates over time. Additionally, the system may rely on unstructured data such as signals from Internet of Things (IoT) devices, web scraping data, or output data from audio-to-text converter. If the maintenance of these tools from upstream producers is not properly declared or flawed patching is implemented, it can unexpectedly degrade the performance of the ML system.\n  * [**Drift in input data**](https://towardsdatascience.com/monitoring-machine-learning-models-in-production-why-and-how-13d07a5ff0c6): Take demand forecasting in the retail industry as an example. The input data can exhibit new distributions periodically (such as the seasonal cycle of purchasing behaviors), gradually (like the inflationary cost of goods from suppliers), and suddenly (the entry of new competitors).\n\n\n* * *\nIn the following sections, we will delve into some great practices for building ML systems and provide illustrative Python code examples to demonstrate their implementation.\nImagine that you would like to build a robust traffic system for the city prepared for traffic peaks, so you collected traffic data from sensors over the past two years. Your goal is to predict the traffic patterns (i.e. the no. of vehicles) for the upcoming six months.\n  * Training dataset: ID, DateTime, and no. of vehicles\n  * Test dataset: ID and DateTime\n\n\n![Photo by Joey Kyber on Unsplash](../images/004.jpg)\nPhoto by [Joey Kyber](https://unsplash.com/@jtkyber1?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\n### 1 Use design patterns for the ML codebase\nTo make the code design more flexible and reusable for future requirements, we can leverage [design patterns](https://en.wikipedia.org/wiki/Software_design_pattern). These patterns serve as the templates for solving common problems in various situations, enabling us to decouple different parts of the codebase. As a result, this helps to improve the comprehension of the codebase and build a common language to communicate quickly about solutions.\nThe two primary components in ML projects are data and algorithms, which can benefit from design patterns.\n  * Factory pattern\n\n\nThis creational pattern provides a layer of abstraction for generating objects at runtime. In ML systems, we can implement this pattern by creating a data loader class (`CSVDataLoader` in this example) that handles the loading, saving, and returning of training/ test data with a consistent data type. We can then declare the `DataProcessor` interface without specifying the concrete implementation.\npython\n\n```\n# CSV Data loader class\nclass CSVDataLoader:\n  def __init__(self, file_path):\n    self.file_path = file_path\n\n  def get_data(self):\n    return pd.read_csv(self.file_path)\n\n  def save_data(self, df):\n    df.to_csv(f'data/transformed_{self.file_path}', index=False)\n\n# Interface\nclass DataProcessor:\n  def __init__(self, train_data_loader, test_data_loader):\n    self.train_data_loader = train_data_loader\n    self.test_data_loader = test_data_loader\n\n  def run(self):\n    # Load training and test data using data loaders\n    train_df = self.train_data_loader.get_data()\n    test_df = self.test_data_loader.get_data()\n\n# Create a data processor instance\nprocess = DataProcessor(\n train_data_loader=CSVDataLoader(file_path='train.csv'),\n test_data_loader=CSVDataLoader(file_path='test.csv')\n)\n\n# Run the data processing pipeline\nprocess.run()\n```\n\nThis approach allows you to extend the code without having to re-implement the `DataProcessor`. For example, if you want to load the dataset from a JSON file, you can simply create a new class `JSONDataLoader` as an instance of the data loader for declaration.\n  * Strategy Pattern\n\n\nSince there is no one-size-fits-all algorithm for ML problems with varying data distribution, we often find ourselves switching and experimenting between algorithms during prototyping or project enhancement. We can apply the Strategy Pattern by creating a new class `DataTransformer` for feature engineering and another class `LGBMModel` for encapsulating the strategy of fitting and predicting using the LightGBM model.\npython\n\n```\nclass DataTransformer:\n  def transform_data(self, train_df, test_df):\n    for idx, df in enumerate([train_df, test_df]):\n      df['DateTime'] = pd.to_datetime(df['DateTime'])\n\n      # Build 'Time' column\n      df['Time'] = [date.hour * 3600 + date.minute * 60 + date.second for date in df['DateTime']]\n\n      # Convert DateTime to Unix timestamp\n      unixtime = [time.mktime(date.timetuple()) for date in df['DateTime']]\n      df['DateTime'] = unixtime\n\n      # Perform one-hot encoding on the DataFrame\n      df = pd.get_dummies(df)\n\n      if idx == 0:\n        # Split training DataFrame into features (X_train) and target (y_train)\n        X_train = df.drop(['Vehicles'], axis=1)\n        y_train = df[['Vehicles']]\n      elif idx == 1:\n        # Store test DataFrame\n        X_test = df\n\n    return X_train, y_train, X_test\n```\n\nruby\n\n```\nclass LGBMModel:\n  def __init__(self, num_leaves, n_estimators):\n    self.model = lgb.LGBMRegressor(\n      num_leaves=num_leaves,\n      n_estimators=n_estimators\n    )\n\n  def fit(self, X, y):\n    self.model.fit(X, y)\n    self.model.booster_.save_model('model/lgbm_model.txt')\n    return self\n\n  def predict(self, X):\n    predictions = self.model.predict(X)\n    return predictions\n```\n\nThe implementation and declaration of the interface `DataProcessor` are provided below. This is the end-to-end process that includes loading training and test data using `train_data_loader` and `test_data_loader`respectively, transforming the data using `data_transformer`, and fitting the model to the transformed data using `model`. As a result, we can predict the number of vehicles in each record of the test dataset.\npython\n\n```\n# Interface\nclass DataProcessor:\n  def __init__(self, train_data_loader, test_data_loader, data_transformer, model):\n    self.train_data_loader = train_data_loader\n    self.test_data_loader = test_data_loader\n    self.data_transformer = data_transformer\n    self.model = model\n\n  def run(self):\n    # Load train and test data using data loaders\n    train_df = self.train_data_loader.get_data()\n    test_df = self.test_data_loader.get_data()\n\n    # Transform the data using the data transformer\n    X_train, y_train, X_test = self.data_transformer.transform_data(train_df, test_df)\n\n    # Fit the model and make prediction\n    self.model.fit(X_train, y_train)\n    test_df['Vehicles'] = self.model.predict(X_test)\n\n    # Save the transformed training data and test data\n    self.train_data_loader.save_data(pd.concat([X_train, y_train], axis=1))\n    self.test_data_loader.save_data(test_df)\n\n# Create a data processor instance\nprocess = DataProcessor(\n    train_data_loader=CSVDataLoader(file_path='train.csv'),\n    test_data_loader=CSVDataLoader(file_path='test.csv'),\n    data_transformer=DataTransformer(),\n    model=LGBMModel(num_leaves=16, n_estimators=80)\n)\n\n# Run the data processing pipeline\nprocess.run()\n```\n\nYou can easily add new blocks of code to implement other data transformation ideas or algorithms. Similar to the Factory Pattern, these changes would not require you to modify the interface `DataProcessor`. This design makes it easier to maintain the code, even if you have a long list of algorithms. The behavior of the ML system can dynamically vary based on the chosen strategy.\nOf course, the above code implementation is only the preliminary template for development. For example, we can further enhance the code by covering data validation, implementing a hyperparameter tuning mechanism, and evaluating the model.\n### 2 Version control of the ML systems\nThroughout the complex process of model development and management, we require proper [version control](https://en.wikipedia.org/wiki/Version_control). This enables us to maintain a history of modifications done by ourselves or team members and track the version in the local environment relative to the components of the ML system, including data, trained models, and hyperparameters. We can thus address some common questions, including:\n  * What was the change that led to the model's failure?\n  * Which modifications resulted in improved model performance?\n  * Which version of the model was most recently released?\n\n\nHere we demonstrate how to utilize the versioning features in [DVC](https://dvc.org/doc/use-cases), which works best in the [Git](https://git-scm.com/) repository for tracking our original traffic data, transformed traffic data, and LGBM models.\nshell\n\n```\n# Initialise a Git and DVC project in the current working directory\ngit init\ndvc init\n\n# Capture the current state of transformed data in folder 'data' and latest LGBM model in folder 'model'\ndvc add data model\n\n# Commit the current state of 1st version\ngit add data.dvc model.dvc .gitignore\ngit commit -m \"First LGBM model, with Time feature\"\ngit tag -a \"v1.0\" -m \"model v1.0, Time feature\"\n```\n\nLet's consider a scenario where we have made the following changes in the second version of data processing:\n  * Add the Weekday feature in the `DataTransformer` class\n\n\npython\n\n```\ndf['Weekday'] = [datetime.weekday(date) for date in df.DateTime]\n```\n\n  * Set new configuration parameters for the LGBM model in the `DataProcessor` interface\n\n\npython\n\n```\nmodel=LGBMModel(num_leaves=20, n_estimators=90)\n```\n\nWith the following commands, we can track the second version of the data and model in DVC and commit the .dvc files that point to them with Git.\nshell\n\n```\ngit add data.dvc model.dvc\ngit commit -m \"Second LGBM model, with Time and Weekday features\"\ngit tag -a \"v2.0\" -m \"model v2.0, Time and Weakday features\"\n```\n\nThough the workspace is currently locating the second version of our data and model, we can easily switch back and restore to the first snapshot whenever necessary.\nshell\n\n```\ngit checkout v1.0\ndvc checkout\n```\n\nThe above commands cover the basic operations. We can further leverage the tool for project organization and collaboration. Examples of use cases are to understand how the data was built initially and compare model metrics among experiments.\n### 3 Test and monitor the ML systems continuously\nOnce the enhanced model can generate predictions, it is essential to perform [sanity checks](https://en.wikipedia.org/wiki/Sanity_check) before releasing it to production. This is achieved by fitting a random sample set of online data into the latest model offline and examining the results from various perspectives.\n  * **Ensuring the right access permission** : The model results can be stored in the destination path (such as writing them to a Hive table).\n  * **Eliminating**[**semantic errors**](https://www.encyclopedia.com/computing/dictionaries-thesauruses-pictures-and-press-releases/semantic-error): Visualize the distribution of the transformed features that are fitted to the model, to identify any deviant behavior.\n  * **Assessing model performance** : Rescore with the latest model and compare the results to the current online model using appropriate performance metrics (e.g., F1-score is a preferable measurement to accuracy for imbalanced class problems).\n\n\nEven after the latest version of the ML system is released, ongoing monitoring is necessary to account for evolving external environments.\n  * [**Monitoring data drift and model drift**](https://medium.com/towards-data-science/monitoring-machine-learning-models-in-production-why-and-how-13d07a5ff0c6): Detect drift conditions through model performance metrics, statistical tests, and adaptive windowing techniques.\n  * **Tracking upstream producers** : Stay informed about changes in upstream processes, and routinely monitor them to ensure they meet a service level objective.\n\n\n### **Wrapping it up**\nWe have explored several effective practices that can be implemented to tackle technical debts in developing and deploying ML systems.\n  * Use design patterns, to create a modular and flexible data processing pipeline that can adapt to unforeseen requirements.\n  * Utilize version control, to track and manage the ML artifacts, such as data and models, ensuring a workflow with less messiness.\n  * Test and monitor the ML system, to promptly and smoothly handle changes in the dynamic external world.\n\n\n* * *\n### Before you go\nIf you enjoy this reading, I invite you to **** follow my [Medium page](https://medium.com/@johnleungTJ) and [Linkedin page](https://www.linkedin.com/in/john-leung-639800115/). By doing so, you can stay updated with exciting content related to data science side projects, Machine Learning Operations (MLOps) demonstrations, and project management methodologies.\n> [**Monitoring Machine Learning Models in Production: Why and How?**](https://towardsdatascience.com/monitoring-machine-learning-models-in-production-why-and-how-13d07a5ff0c6)\n> [**Optimizing Your Strategies with Approaches Beyond A/B Testing**](https://towardsdatascience.com/optimizing-your-strategies-with-approaches-beyond-a-b-testing-bf11508f8930)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](../images/body-4766f66da8cde6940868.jpeg)\nWritten By\n## [John Leung](https://towardsdatascience.com/author/johnleungtj/)\n[See all from John Leung](https://towardsdatascience.com/author/johnleungtj/)\n[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Python](https://towardsdatascience.com/tag/python/)[Technical Debt](https://towardsdatascience.com/tag/technical-debt/)[Version Control](https://towardsdatascience.com/tag/version-control/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fmanaging-the-technical-debts-of-machine-learning-systems-5b85d420ab9d%2F&title=Managing%20the%20Technical%20Debts%20of%20Machine%20Learning%20Systems)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fmanaging-the-technical-debts-of-machine-learning-systems-5b85d420ab9d%2F&title=Managing%20the%20Technical%20Debts%20of%20Machine%20Learning%20Systems)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fmanaging-the-technical-debts-of-machine-learning-systems-5b85d420ab9d%2F&text=Managing%20the%20Technical%20Debts%20of%20Machine%20Learning%20Systems)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](../images/005.jpeg)](https://towardsdatascience.com/exploring-google-analytics-realtime-data-with-python-8625849c7d7a/)\n## [Exploring Google Analytics Realtime Data with Python](https://towardsdatascience.com/exploring-google-analytics-realtime-data-with-python-8625849c7d7a/)\n[Programming](https://towardsdatascience.com/category/programming/)\nTake full advantage of all the Google Analytics features and data using its REST API and Python\n[Martin Heinz](https://towardsdatascience.com/author/martin.heinz/)\nNovember 29, 20218 min read\n  * [![](../images/006.jpeg)](https://towardsdatascience.com/top-7-machine-learning-resources-i-wish-i-knew-earlier-89f4b8d3364c/)\n## [Top 7 Machine Learning resources I wish I knew earlier](https://towardsdatascience.com/top-7-machine-learning-resources-i-wish-i-knew-earlier-89f4b8d3364c/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nNot teaching materials, just useful websites\n[Dmytro Nikolaiev (Dimid)](https://towardsdatascience.com/author/andimid/)\nApril 30, 20228 min read\n  * [![](../images/007.jpeg)](https://towardsdatascience.com/machine-learning-in-breast-cancer-research-d1dd99f7819a/)\n## [Machine Learning in Breast Cancer Research](https://towardsdatascience.com/machine-learning-in-breast-cancer-research-d1dd99f7819a/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nHow explainable machine learning can be applied for pattern recognition in breast cancer research\n[Jarrett Evans](https://towardsdatascience.com/author/jevans2532/)\nAugust 18, 20226 min read\n  * [![](../images/008.png)](https://towardsdatascience.com/execution-times-in-python-ed45ecc1bb4d/)\n## [Execution Times in Python](https://towardsdatascience.com/execution-times-in-python-ed45ecc1bb4d/)\n[](https://towardsdatascience.com/managing-the-technical-debts-of-machine-learning-systems-5b85d420ab9d/)\nMeasure the execution time of your code in Python the right way\n[Diego Barba](https://towardsdatascience.com/author/diego-barba/)\nJune 22, 20226 min read\n  * [![](../images/009.jpeg)](https://towardsdatascience.com/airflow-data-intervals-a-deep-dive-15d0ccfb0661/)\n## [Airflow Data Intervals: A Deep Dive](https://towardsdatascience.com/airflow-data-intervals-a-deep-dive-15d0ccfb0661/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nBuilding idempotent and re-playable data pipelines\n[Giorgos Myrianthous](https://towardsdatascience.com/author/gmyrianthous/)\nJanuary 7, 20259 min read\n  * [![](../images/010.jpeg)](https://towardsdatascience.com/6-fundamental-visualizations-for-data-analysis-d38344a51141/)\n## [6 Fundamental Visualizations for Data Analysis](https://towardsdatascience.com/6-fundamental-visualizations-for-data-analysis-d38344a51141/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nA practical guide with Matplotlib\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nDecember 4, 20206 min read\n  * [![](../images/011.jpg)](https://towardsdatascience.com/a-practical-introduction-to-llms-65194dda1148/)\n## [A Practical Introduction to LLMs](https://towardsdatascience.com/a-practical-introduction-to-llms-65194dda1148/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n3 levels of using LLMs in practice\n[Shaw Talebi](https://towardsdatascience.com/author/shawhin/)\nJuly 13, 20237 min read\n  * [![](../images/012.jpg)](https://towardsdatascience.com/tuning-with-hdbscan-149865ac2970/)\n## [Tuning with HDBSCAN](https://towardsdatascience.com/tuning-with-hdbscan-149865ac2970/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nA Quick Example of How to Tune Density Based Clustering from the Trenches Clustering is...\n[Charles Frenzel](https://towardsdatascience.com/author/charles-frenzel/)\nSeptember 2, 20217 min read\n  * [![](../images/013.jpeg)](https://towardsdatascience.com/understand-bayes-rule-likelihood-prior-and-posterior-34eae0f378c5/)\n## [Understand Bayes Rule, Likelihood, Prior and Posterior](https://towardsdatascience.com/understand-bayes-rule-likelihood-prior-and-posterior-34eae0f378c5/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nA case study based introduction to using Bayes rule and how it compares with a frequentist, pessimistic and optimistic approaches to...\n[Ahmar Shah, PhD (Oxford)](https://towardsdatascience.com/author/ahmarshah/)\nDecember 25, 20209 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n\n\n",
  "metadata": {
    "title": "Managing the Technical Debts of Machine Learning Systems | Towards Data Science",
    "description": "Explore the practices with implementation codes for sustainably mitigating the cost of speedy delivery",
    "keywords": null,
    "author": "John Leung",
    "og:locale": "en_US",
    "og:type": "article",
    "og:title": "Managing the Technical Debts of Machine Learning Systems",
    "og:description": "Explore the practices with implementation codes for sustainably mitigating the cost of speedy delivery",
    "og:url": "https://towardsdatascience.com/managing-the-technical-debts-of-machine-learning-systems-5b85d420ab9d/",
    "og:site_name": "Towards Data Science",
    "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2023/09/0oZwQUglQJgSc2DTU-scaled.jpg",
    "og:image:width": "2560",
    "og:image:height": "1707",
    "og:image:type": "image/jpeg",
    "twitter:card": "summary_large_image",
    "twitter:site": "@TDataScience",
    "twitter:creator": "@TDataScience",
    "twitter:label1": "Written by",
    "twitter:data1": "John Leung",
    "twitter:label2": "Est. reading time",
    "twitter:data2": "10 min read",
    "article:published_time": "2023-09-26T06:16:36.000Z",
    "article:modified_time": "2023-09-26T06:16:36.000Z",
    "article:author": "https://towardsdatascience.com/author/johnleungtj/"
  },
  "paywall": {
    "listed_paid": false,
    "detected": false,
    "marker": "",
    "public_only": false,
    "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙"
  },
  "crawl": {
    "success": true,
    "status_code": 202,
    "error": ""
  },
  "code_blocks": [
    {
      "language": "txt",
      "file": "attachments/b5a28259ab43/code/block_000.txt",
      "chars": 840
    },
    {
      "language": "txt",
      "file": "attachments/b5a28259ab43/code/block_001.txt",
      "chars": 827
    },
    {
      "language": "txt",
      "file": "attachments/b5a28259ab43/code/block_002.txt",
      "chars": 380
    },
    {
      "language": "txt",
      "file": "attachments/b5a28259ab43/code/block_003.txt",
      "chars": 1212
    },
    {
      "language": "txt",
      "file": "attachments/b5a28259ab43/code/block_004.txt",
      "chars": 392
    },
    {
      "language": "txt",
      "file": "attachments/b5a28259ab43/code/block_005.txt",
      "chars": 65
    },
    {
      "language": "txt",
      "file": "attachments/b5a28259ab43/code/block_006.txt",
      "chars": 48
    },
    {
      "language": "txt",
      "file": "attachments/b5a28259ab43/code/block_007.txt",
      "chars": 154
    },
    {
      "language": "txt",
      "file": "attachments/b5a28259ab43/code/block_008.txt",
      "chars": 31
    }
  ],
  "images": [
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T18:01:49Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T18:01:49Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T18:01:50Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T18:01:50Z"
    }
  ],
  "audio": [],
  "videos": [
    {
      "url": "https://www.youtube.com/c/TowardsDataScience",
      "kind": "channel_reference",
      "status": "reference_only"
    }
  ],
  "links": [
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/latest/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/editors-pick/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/the-variable/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/shipai/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/submissions/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/johnleungtj/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/monitoring-machine-learning-models-in-production-why-and-how-13d07a5ff0c6"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/optimizing-your-strategies-with-approaches-beyond-a-b-testing-bf11508f8930"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/artificial-intelligence/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/python/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/technical-debt/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/version-control/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/exploring-google-analytics-realtime-data-with-python-8625849c7d7a/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/programming/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/martin.heinz/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/top-7-machine-learning-resources-i-wish-i-knew-earlier-89f4b8d3364c/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/artificial-intelligence/machine-learning/"
    },
    {
      "type": "external",
      "href": "https://write.insightmediagroup.io"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/company/towards-data-science/"
    },
    {
      "type": "external",
      "href": "https://x.com/TDataScience"
    },
    {
      "type": "external",
      "href": "https://scikit-learn.org/stable/"
    },
    {
      "type": "external",
      "href": "https://huggingface.co/models?sort=downloads"
    },
    {
      "type": "external",
      "href": "https://unsplash.com/@santesson89"
    },
    {
      "type": "external",
      "href": "https://unsplash.com"
    },
    {
      "type": "external",
      "href": "https://en.wikipedia.org/wiki/Proof_of_concept"
    },
    {
      "type": "external",
      "href": "https://en.wikipedia.org/wiki/Glue_code"
    },
    {
      "type": "external",
      "href": "https://unsplash.com/@jakobowens1"
    },
    {
      "type": "external",
      "href": "https://unsplash.com/@ricardoviana"
    },
    {
      "type": "external",
      "href": "https://unsplash.com/@jtkyber1"
    },
    {
      "type": "external",
      "href": "https://en.wikipedia.org/wiki/Software_design_pattern"
    },
    {
      "type": "external",
      "href": "https://en.wikipedia.org/wiki/Version_control"
    },
    {
      "type": "external",
      "href": "https://dvc.org/doc/use-cases"
    },
    {
      "type": "external",
      "href": "https://git-scm.com/"
    },
    {
      "type": "external",
      "href": "https://en.wikipedia.org/wiki/Sanity_check"
    },
    {
      "type": "external",
      "href": "https://www.encyclopedia.com/computing/dictionaries-thesauruses-pictures-and-press-releases/semantic-error"
    },
    {
      "type": "external",
      "href": "https://medium.com/towards-data-science/monitoring-machine-learning-models-in-production-why-and-how-13d07a5ff0c6"
    },
    {
      "type": "external",
      "href": "https://medium.com/@johnleungTJ"
    }
  ],
  "repair_version": 2,
  "repaired_at": "2026-10-20T17:30:15Z",
  "source_url": "https://towardsdatascience.com/managing-the-technical-debts-of-machine-learning-systems-5b85d420ab9d/",
  "delivery_status": "unverified",
  "discovery_status": "http_403",
  "missing_reason": "源页面未重新核验:http_403；已按保留正文补采图片",
  "repair_error": "",
  "actual_video_files": [],
  "expected_saved_body_images": 4,
  "image_discovery": "saved_markdown",
  "source_body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](https://cdn-cookieyes.com/assets/images/close.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Managing the Technical Debts of Machine Learning Systems\nExplore the practices with implementation codes for sustainably mitigating the cost of speedy delivery\n[John Leung](https://towardsdatascience.com/author/johnleungtj/)\nSeptember 26, 202310 min read\n# Managing the Technical Debt of Machine Learning Systems\n### Explore the practices (design patterns, version control, and monitoring systems) for sustainably mitigating the cost of speedy delivery—with implementation codes\nAs the machine learning (ML) community advances over the years, the resources available for developing ML projects are plentiful. For example, we can rely on the generic Python package [scikit-learn](https://scikit-learn.org/stable/), which is built on NumPy, SciPy, and matplotlib, for data preprocessing and basic predictive tasks. Or we can leverage the open-source collection of [pre-trained models](https://huggingface.co/models?sort=downloads) from Hugging Face for analyzing diverse types of datasets. These empower current data scientists to quickly and effortlessly tackle standard ML tasks while achieving moderately good model performance.\nHowever, the abundance of ML tools often leads business stakeholders and even practitioners to underestimate the effort required to build enterprise-level ML systems. Particularly when faced with tight project deadlines, the teams may expedite deploying systems to production without giving sufficient technical considerations. Consequently, the ML system often does not address the business needs in a technically sustainable and maintainable manner.\n> As the system evolves and deploys over time, technical debts accumulate - The longer the implied cost remains unaddressed, the more costly it becomes to rectify them.\n![Photo by Andrea De Santis on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/09/0oZwQUglQJgSc2DTU-scaled.jpg)\nPhoto by [Andrea De Santis](https://unsplash.com/@santesson89?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nThere are multiple sources of technical debts in the ML system. Some are included below.\n### 1 Inflexible code design to cater to unforeseen requirements\nTo validate if ML can address the enterprise challenges at hand, many ML projects commence with a [proof of concept (PoC)](https://en.wikipedia.org/wiki/Proof_of_concept). We initially created a Jupyter Notebook or Google Colab environment to explore data, then developed several ad-hoc functions, and created the illusion of nearing project completion for stakeholders. **Such systems building directly from PoC may end up consisting mostly of**[**glue code**](https://en.wikipedia.org/wiki/Glue_code) - the supporting code that connects specific incompatible components but itself does not have the functionality of data analysis. They can be spaghetti-like, hard to maintain, and prone to errors.\n![Photo by Jakob Owens on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/09/0SHhYzZv1bIPhY4SX-scaled.jpg)\nPhoto by [Jakob Owens](https://unsplash.com/@jakobowens1?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nBusiness stakeholders often present alternate requirements from time to time or wish to scale up the project, such as trying out new data sources or new algorithms. We thus often find ourselves frequently revisiting the codebase covering the current preprocessing pipelines and model development processes. Inflexible code design can consequently lead to difficulties in reacting to new changes, or rewrite most of the codes for even minor adjustments.\n### 2 Messiness in the configurations of the ML system\nSoftware engineering programming automates tasks by defining rules for computers to follow, ensuring precise output for the same input. Software engineers are also concerned with the correctness of every corner case. On the other hand, ML systems automate tasks by collecting and feeding feature data into models to achieve desired target results. This experimental process embraces uncertainty and variability. **As ML systems mature, they often contain multiple versions of configuration options** , such as datasets with different feature combinations, and algorithm-specific learning settings.\n![Photo by Ricardo Viana on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/09/0uBBsVieXPG2uFQ8B-scaled.jpg)\nPhoto by [Ricardo Viana](https://unsplash.com/@ricardoviana?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nInput features in ML systems are inherently interconnected. Consider a scenario where feature A is no longer available as an input for your ML systems in production, you are required to re-evaluate the weighting of the remaining features for production serving. However, after 2 months, feature A becomes available again. If you did not systematically save or even mistakenly modify the original configurations, rectifying the decrease in performance would require additional computational resources and time effort.\n### 3 Limited ability to adapt to the evolving external world\nML systems often have dependencies on the external world, and various hidden factors continually evolve but are not being appropriately considered and monitored.\n![External factors leading to potential degradation of model performance \\(image by author\\)](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/09/1CnlWbSYerdAicuFarzb1ZQ.png)\nExternal factors leading to potential degradation of model performance (image by author)\n  * **Unstable data output from upstream producers** : The input signal of our ML system may come from another machine learning model that updates over time. Additionally, the system may rely on unstructured data such as signals from Internet of Things (IoT) devices, web scraping data, or output data from audio-to-text converter. If the maintenance of these tools from upstream producers is not properly declared or flawed patching is implemented, it can unexpectedly degrade the performance of the ML system.\n  * [**Drift in input data**](https://towardsdatascience.com/monitoring-machine-learning-models-in-production-why-and-how-13d07a5ff0c6): Take demand forecasting in the retail industry as an example. The input data can exhibit new distributions periodically (such as the seasonal cycle of purchasing behaviors), gradually (like the inflationary cost of goods from suppliers), and suddenly (the entry of new competitors).\n\n\n* * *\nIn the following sections, we will delve into some great practices for building ML systems and provide illustrative Python code examples to demonstrate their implementation.\nImagine that you would like to build a robust traffic system for the city prepared for traffic peaks, so you collected traffic data from sensors over the past two years. Your goal is to predict the traffic patterns (i.e. the no. of vehicles) for the upcoming six months.\n  * Training dataset: ID, DateTime, and no. of vehicles\n  * Test dataset: ID and DateTime\n\n\n![Photo by Joey Kyber on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/09/01_WgXfmu9nMf6xEg-scaled.jpg)\nPhoto by [Joey Kyber](https://unsplash.com/@jtkyber1?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\n### 1 Use design patterns for the ML codebase\nTo make the code design more flexible and reusable for future requirements, we can leverage [design patterns](https://en.wikipedia.org/wiki/Software_design_pattern). These patterns serve as the templates for solving common problems in various situations, enabling us to decouple different parts of the codebase. As a result, this helps to improve the comprehension of the codebase and build a common language to communicate quickly about solutions.\nThe two primary components in ML projects are data and algorithms, which can benefit from design patterns.\n  * Factory pattern\n\n\nThis creational pattern provides a layer of abstraction for generating objects at runtime. In ML systems, we can implement this pattern by creating a data loader class (`CSVDataLoader` in this example) that handles the loading, saving, and returning of training/ test data with a consistent data type. We can then declare the `DataProcessor` interface without specifying the concrete implementation.\npython\n\n```\n# CSV Data loader class\nclass CSVDataLoader:\n  def __init__(self, file_path):\n    self.file_path = file_path\n\n  def get_data(self):\n    return pd.read_csv(self.file_path)\n\n  def save_data(self, df):\n    df.to_csv(f'data/transformed_{self.file_path}', index=False)\n\n# Interface\nclass DataProcessor:\n  def __init__(self, train_data_loader, test_data_loader):\n    self.train_data_loader = train_data_loader\n    self.test_data_loader = test_data_loader\n\n  def run(self):\n    # Load training and test data using data loaders\n    train_df = self.train_data_loader.get_data()\n    test_df = self.test_data_loader.get_data()\n\n# Create a data processor instance\nprocess = DataProcessor(\n train_data_loader=CSVDataLoader(file_path='train.csv'),\n test_data_loader=CSVDataLoader(file_path='test.csv')\n)\n\n# Run the data processing pipeline\nprocess.run()\n```\n\nThis approach allows you to extend the code without having to re-implement the `DataProcessor`. For example, if you want to load the dataset from a JSON file, you can simply create a new class `JSONDataLoader` as an instance of the data loader for declaration.\n  * Strategy Pattern\n\n\nSince there is no one-size-fits-all algorithm for ML problems with varying data distribution, we often find ourselves switching and experimenting between algorithms during prototyping or project enhancement. We can apply the Strategy Pattern by creating a new class `DataTransformer` for feature engineering and another class `LGBMModel` for encapsulating the strategy of fitting and predicting using the LightGBM model.\npython\n\n```\nclass DataTransformer:\n  def transform_data(self, train_df, test_df):\n    for idx, df in enumerate([train_df, test_df]):\n      df['DateTime'] = pd.to_datetime(df['DateTime'])\n\n      # Build 'Time' column\n      df['Time'] = [date.hour * 3600 + date.minute * 60 + date.second for date in df['DateTime']]\n\n      # Convert DateTime to Unix timestamp\n      unixtime = [time.mktime(date.timetuple()) for date in df['DateTime']]\n      df['DateTime'] = unixtime\n\n      # Perform one-hot encoding on the DataFrame\n      df = pd.get_dummies(df)\n\n      if idx == 0:\n        # Split training DataFrame into features (X_train) and target (y_train)\n        X_train = df.drop(['Vehicles'], axis=1)\n        y_train = df[['Vehicles']]\n      elif idx == 1:\n        # Store test DataFrame\n        X_test = df\n\n    return X_train, y_train, X_test\n```\n\nruby\n\n```\nclass LGBMModel:\n  def __init__(self, num_leaves, n_estimators):\n    self.model = lgb.LGBMRegressor(\n      num_leaves=num_leaves,\n      n_estimators=n_estimators\n    )\n\n  def fit(self, X, y):\n    self.model.fit(X, y)\n    self.model.booster_.save_model('model/lgbm_model.txt')\n    return self\n\n  def predict(self, X):\n    predictions = self.model.predict(X)\n    return predictions\n```\n\nThe implementation and declaration of the interface `DataProcessor` are provided below. This is the end-to-end process that includes loading training and test data using `train_data_loader` and `test_data_loader`respectively, transforming the data using `data_transformer`, and fitting the model to the transformed data using `model`. As a result, we can predict the number of vehicles in each record of the test dataset.\npython\n\n```\n# Interface\nclass DataProcessor:\n  def __init__(self, train_data_loader, test_data_loader, data_transformer, model):\n    self.train_data_loader = train_data_loader\n    self.test_data_loader = test_data_loader\n    self.data_transformer = data_transformer\n    self.model = model\n\n  def run(self):\n    # Load train and test data using data loaders\n    train_df = self.train_data_loader.get_data()\n    test_df = self.test_data_loader.get_data()\n\n    # Transform the data using the data transformer\n    X_train, y_train, X_test = self.data_transformer.transform_data(train_df, test_df)\n\n    # Fit the model and make prediction\n    self.model.fit(X_train, y_train)\n    test_df['Vehicles'] = self.model.predict(X_test)\n\n    # Save the transformed training data and test data\n    self.train_data_loader.save_data(pd.concat([X_train, y_train], axis=1))\n    self.test_data_loader.save_data(test_df)\n\n# Create a data processor instance\nprocess = DataProcessor(\n    train_data_loader=CSVDataLoader(file_path='train.csv'),\n    test_data_loader=CSVDataLoader(file_path='test.csv'),\n    data_transformer=DataTransformer(),\n    model=LGBMModel(num_leaves=16, n_estimators=80)\n)\n\n# Run the data processing pipeline\nprocess.run()\n```\n\nYou can easily add new blocks of code to implement other data transformation ideas or algorithms. Similar to the Factory Pattern, these changes would not require you to modify the interface `DataProcessor`. This design makes it easier to maintain the code, even if you have a long list of algorithms. The behavior of the ML system can dynamically vary based on the chosen strategy.\nOf course, the above code implementation is only the preliminary template for development. For example, we can further enhance the code by covering data validation, implementing a hyperparameter tuning mechanism, and evaluating the model.\n### 2 Version control of the ML systems\nThroughout the complex process of model development and management, we require proper [version control](https://en.wikipedia.org/wiki/Version_control). This enables us to maintain a history of modifications done by ourselves or team members and track the version in the local environment relative to the components of the ML system, including data, trained models, and hyperparameters. We can thus address some common questions, including:\n  * What was the change that led to the model's failure?\n  * Which modifications resulted in improved model performance?\n  * Which version of the model was most recently released?\n\n\nHere we demonstrate how to utilize the versioning features in [DVC](https://dvc.org/doc/use-cases), which works best in the [Git](https://git-scm.com/) repository for tracking our original traffic data, transformed traffic data, and LGBM models.\nshell\n\n```\n# Initialise a Git and DVC project in the current working directory\ngit init\ndvc init\n\n# Capture the current state of transformed data in folder 'data' and latest LGBM model in folder 'model'\ndvc add data model\n\n# Commit the current state of 1st version\ngit add data.dvc model.dvc .gitignore\ngit commit -m \"First LGBM model, with Time feature\"\ngit tag -a \"v1.0\" -m \"model v1.0, Time feature\"\n```\n\nLet's consider a scenario where we have made the following changes in the second version of data processing:\n  * Add the Weekday feature in the `DataTransformer` class\n\n\npython\n\n```\ndf['Weekday'] = [datetime.weekday(date) for date in df.DateTime]\n```\n\n  * Set new configuration parameters for the LGBM model in the `DataProcessor` interface\n\n\npython\n\n```\nmodel=LGBMModel(num_leaves=20, n_estimators=90)\n```\n\nWith the following commands, we can track the second version of the data and model in DVC and commit the .dvc files that point to them with Git.\nshell\n\n```\ngit add data.dvc model.dvc\ngit commit -m \"Second LGBM model, with Time and Weekday features\"\ngit tag -a \"v2.0\" -m \"model v2.0, Time and Weakday features\"\n```\n\nThough the workspace is currently locating the second version of our data and model, we can easily switch back and restore to the first snapshot whenever necessary.\nshell\n\n```\ngit checkout v1.0\ndvc checkout\n```\n\nThe above commands cover the basic operations. We can further leverage the tool for project organization and collaboration. Examples of use cases are to understand how the data was built initially and compare model metrics among experiments.\n### 3 Test and monitor the ML systems continuously\nOnce the enhanced model can generate predictions, it is essential to perform [sanity checks](https://en.wikipedia.org/wiki/Sanity_check) before releasing it to production. This is achieved by fitting a random sample set of online data into the latest model offline and examining the results from various perspectives.\n  * **Ensuring the right access permission** : The model results can be stored in the destination path (such as writing them to a Hive table).\n  * **Eliminating**[**semantic errors**](https://www.encyclopedia.com/computing/dictionaries-thesauruses-pictures-and-press-releases/semantic-error): Visualize the distribution of the transformed features that are fitted to the model, to identify any deviant behavior.\n  * **Assessing model performance** : Rescore with the latest model and compare the results to the current online model using appropriate performance metrics (e.g., F1-score is a preferable measurement to accuracy for imbalanced class problems).\n\n\nEven after the latest version of the ML system is released, ongoing monitoring is necessary to account for evolving external environments.\n  * [**Monitoring data drift and model drift**](https://medium.com/towards-data-science/monitoring-machine-learning-models-in-production-why-and-how-13d07a5ff0c6): Detect drift conditions through model performance metrics, statistical tests, and adaptive windowing techniques.\n  * **Tracking upstream producers** : Stay informed about changes in upstream processes, and routinely monitor them to ensure they meet a service level objective.\n\n\n### **Wrapping it up**\nWe have explored several effective practices that can be implemented to tackle technical debts in developing and deploying ML systems.\n  * Use design patterns, to create a modular and flexible data processing pipeline that can adapt to unforeseen requirements.\n  * Utilize version control, to track and manage the ML artifacts, such as data and models, ensuring a workflow with less messiness.\n  * Test and monitor the ML system, to promptly and smoothly handle changes in the dynamic external world.\n\n\n* * *\n### Before you go\nIf you enjoy this reading, I invite you to **** follow my [Medium page](https://medium.com/@johnleungTJ) and [Linkedin page](https://www.linkedin.com/in/john-leung-639800115/). By doing so, you can stay updated with exciting content related to data science side projects, Machine Learning Operations (MLOps) demonstrations, and project management methodologies.\n> [**Monitoring Machine Learning Models in Production: Why and How?**](https://towardsdatascience.com/monitoring-machine-learning-models-in-production-why-and-how-13d07a5ff0c6)\n> [**Optimizing Your Strategies with Approaches Beyond A/B Testing**](https://towardsdatascience.com/optimizing-your-strategies-with-approaches-beyond-a-b-testing-bf11508f8930)\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657188043_vu8s9b.jpeg)\nWritten By\n## [John Leung](https://towardsdatascience.com/author/johnleungtj/)\n[See all from John Leung](https://towardsdatascience.com/author/johnleungtj/)\n[Artificial Intelligence](https://towardsdatascience.com/tag/artificial-intelligence/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Python](https://towardsdatascience.com/tag/python/)[Technical Debt](https://towardsdatascience.com/tag/technical-debt/)[Version Control](https://towardsdatascience.com/tag/version-control/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fmanaging-the-technical-debts-of-machine-learning-systems-5b85d420ab9d%2F&title=Managing%20the%20Technical%20Debts%20of%20Machine%20Learning%20Systems)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fmanaging-the-technical-debts-of-machine-learning-systems-5b85d420ab9d%2F&title=Managing%20the%20Technical%20Debts%20of%20Machine%20Learning%20Systems)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fmanaging-the-technical-debts-of-machine-learning-systems-5b85d420ab9d%2F&text=Managing%20the%20Technical%20Debts%20of%20Machine%20Learning%20Systems)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/02/1Oy6-Ti0E-YfqVXlIn2IreQ-scaled.jpeg)](https://towardsdatascience.com/exploring-google-analytics-realtime-data-with-python-8625849c7d7a/)\n## [Exploring Google Analytics Realtime Data with Python](https://towardsdatascience.com/exploring-google-analytics-realtime-data-with-python-8625849c7d7a/)\n[Programming](https://towardsdatascience.com/category/programming/)\nTake full advantage of all the Google Analytics features and data using its REST API and Python\n[Martin Heinz](https://towardsdatascience.com/author/martin.heinz/)\nNovember 29, 20218 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/04/1WYfQvU9fEnHEnxFxZQo9Kg.jpeg)](https://towardsdatascience.com/top-7-machine-learning-resources-i-wish-i-knew-earlier-89f4b8d3364c/)\n## [Top 7 Machine Learning resources I wish I knew earlier](https://towardsdatascience.com/top-7-machine-learning-resources-i-wish-i-knew-earlier-89f4b8d3364c/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nNot teaching materials, just useful websites\n[Dmytro Nikolaiev (Dimid)](https://towardsdatascience.com/author/andimid/)\nApril 30, 20228 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/08/1RH9VkieJPgGUmBasas8fQA-scaled.jpeg)](https://towardsdatascience.com/machine-learning-in-breast-cancer-research-d1dd99f7819a/)\n## [Machine Learning in Breast Cancer Research](https://towardsdatascience.com/machine-learning-in-breast-cancer-research-d1dd99f7819a/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nHow explainable machine learning can be applied for pattern recognition in breast cancer research\n[Jarrett Evans](https://towardsdatascience.com/author/jevans2532/)\nAugust 18, 20226 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/06/16p5MCB7VTtMRJipqv8_Jdw.png)](https://towardsdatascience.com/execution-times-in-python-ed45ecc1bb4d/)\n## [Execution Times in Python](https://towardsdatascience.com/execution-times-in-python-ed45ecc1bb4d/)\n[](https://towardsdatascience.com/managing-the-technical-debts-of-machine-learning-systems-5b85d420ab9d/)\nMeasure the execution time of your code in Python the right way\n[Diego Barba](https://towardsdatascience.com/author/diego-barba/)\nJune 22, 20226 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2025/01/1pQTOlu_xUN2zl6i_w8etOQ-scaled.jpeg)](https://towardsdatascience.com/airflow-data-intervals-a-deep-dive-15d0ccfb0661/)\n## [Airflow Data Intervals: A Deep Dive](https://towardsdatascience.com/airflow-data-intervals-a-deep-dive-15d0ccfb0661/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nBuilding idempotent and re-playable data pipelines\n[Giorgos Myrianthous](https://towardsdatascience.com/author/gmyrianthous/)\nJanuary 7, 20259 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/12/0s_Lww_u66gLjnkFE-scaled.jpeg)](https://towardsdatascience.com/6-fundamental-visualizations-for-data-analysis-d38344a51141/)\n## [6 Fundamental Visualizations for Data Analysis](https://towardsdatascience.com/6-fundamental-visualizations-for-data-analysis-d38344a51141/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nA practical guide with Matplotlib\n[Soner Yıldırım](https://towardsdatascience.com/author/sonery/)\nDecember 4, 20206 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/0Nl-5C1WBW4XdGkNI-scaled.jpg)](https://towardsdatascience.com/a-practical-introduction-to-llms-65194dda1148/)\n## [A Practical Introduction to LLMs](https://towardsdatascience.com/a-practical-introduction-to-llms-65194dda1148/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n3 levels of using LLMs in practice\n[Shaw Talebi](https://towardsdatascience.com/author/shawhin/)\nJuly 13, 20237 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/09/0fI1r7zSY71B3ujgW.jpg)](https://towardsdatascience.com/tuning-with-hdbscan-149865ac2970/)\n## [Tuning with HDBSCAN](https://towardsdatascience.com/tuning-with-hdbscan-149865ac2970/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nA Quick Example of How to Tune Density Based Clustering from the Trenches Clustering is...\n[Charles Frenzel](https://towardsdatascience.com/author/charles-frenzel/)\nSeptember 2, 20217 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/12/1dINnKtKPYkD0tsm_0Qf8aQ.jpeg)](https://towardsdatascience.com/understand-bayes-rule-likelihood-prior-and-posterior-34eae0f378c5/)\n## [Understand Bayes Rule, Likelihood, Prior and Posterior](https://towardsdatascience.com/understand-bayes-rule-likelihood-prior-and-posterior-34eae0f378c5/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nA case study based introduction to using Bayes rule and how it compares with a frequentist, pessimistic and optimistic approaches to...\n[Ahmar Shah, PhD (Oxford)](https://towardsdatascience.com/author/ahmarshah/)\nDecember 25, 20209 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n\n\n",
  "body_base_path": "attachments/b5a28259ab43/pages/",
  "body_markdown_path": "attachments/b5a28259ab43/pages/archived-body.md",
  "local_html": "attachments/b5a28259ab43/pages/archived-body.html",
  "body_source": "prior_capture"
}