![Revisit consent button](../images/body-df29beacd7496220cc94.svg) We value your privacy We 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. Customise Reject All Accept All Customise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg) We use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below. The 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 NecessaryAlways Active Necessary 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. * Cookie BCTempID * Duration 10 minutes * Description No description available. * Cookie __cf_bm * Duration 1 hour * Description This cookie, set by Cloudflare, is used to support Cloudflare Bot Management. * Cookie AWSALBCORS * Duration 7 days * Description Amazon Web Services set this cookie for load balancing. * Cookie _cfuvid * Duration session * Description Cloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services * Cookie li_gc * Duration 6 months * Description Linkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes. * Cookie __hssrc * Duration session * Description This 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. * Cookie __hssc * Duration 1 hour * Description HubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie. * Cookie wpEmojiSettingsSupports * Duration session * Description WordPress 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. * Cookie BCSessionID * Duration 1 year 1 month 4 days * Description Blueconic sets this cookie as a unique identifier for the BlueConic profile. * Cookie _octo * Duration 1 year * Description No description available. * Cookie logged_in * Duration 1 year * Description No description available. * Cookie __Secure-YEC * Duration past * Description YouTube sets this cookie to stores the user's video player preferences using embedded YouTube video * Cookie __eoi * Duration 6 months * Description Description is currently not available. * Cookie AWSALBTGCORS * Duration 7 days * Description No description available. * Cookie login-status-p * Duration past * Description Description is currently not available. * Cookie AWSALBTG * Duration 7 days * Description No description available. * Cookie csrf_token * Duration session * Description No description available. * Cookie token_v2 * Duration 1 day * Description Description is currently not available. * Cookie D * Duration 1 year * Description Description is currently not available. * Cookie PHPSESSID * Duration session * Description This 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. * Cookie VISITOR_PRIVACY_METADATA * Duration 6 months * Description YouTube sets this cookie to store the user's cookie consent state for the current domain. * Cookie cookietest * Duration session * Description The cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience. * Cookie __Host-airtable-session * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie __Host-airtable-session.sig * Duration 1 year * Description This cookie is used to enable us to integrate the services of Airtable. * Cookie m * Duration 1 year 1 month 4 days * Description Stripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly. * Cookie BIGipServer* * Duration session * Description Marketo 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. * Cookie __cfruid * Duration session * Description Cloudflare sets this cookie to identify trusted web traffic. * Cookie _GRECAPTCHA * Duration 6 months * Description Google Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks. * Cookie __Secure-YNID * Duration 6 months * Description Google cookie used to protect user security and prevent fraud, especially during the login process. * Cookie cookieyes-consent * Duration 1 year * Description CookieYes 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. Functional Functional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features. * Cookie lidc * Duration 1 day * Description LinkedIn sets the lidc cookie to facilitate data center selection. * Cookie brw * Duration 1 year * Description No description available. * Cookie brwConsent * Duration 5 minutes * Description Description is currently not available. * Cookie WMF-Uniq * Duration 1 year * Description Description is currently not available. * Cookie loom_anon_comment * Duration 1 year * Description No description available. * Cookie loom_referral_video * Duration session * Description Description is currently not available. * Cookie VISITOR_INFO1_LIVE * Duration 6 months * Description A cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface. * Cookie yt-remote-connected-devices * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie ytidb::LAST_RESULT_ENTRY_KEY * Duration Never Expires * Description The 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. * Cookie yt-remote-device-id * Duration Never Expires * Description YouTube sets this cookie to store the user's video preferences using embedded YouTube videos. * Cookie yt-remote-session-name * Duration session * Description The yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video. * Cookie yt-remote-fast-check-period * Duration session * Description The yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos. * Cookie yt-remote-session-app * Duration session * Description The yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player. * Cookie yt-remote-cast-available * Duration session * Description The yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player. * Cookie yt-remote-cast-installed * Duration session * Description The yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video. * Cookie cp_session * Duration 3 months * Description Codepen sets this cookie for Help systems found in the website. * Cookie loid * Duration 1 year 1 month 4 days * Description This cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform. Analytics Analytical 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. * Cookie __hstc * Duration 6 months * Description Hubspot 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). * Cookie hubspotutk * Duration 6 months * Description HubSpot 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. * Cookie _ga * Duration 1 year 1 month 4 days * Description Google 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. * Cookie _ga_* * Duration 1 year 1 month 4 days * Description Google Analytics sets this cookie to store and count page views. * Cookie __Host-psifi.analyticsTrace * Duration 6 hours * Description Description is currently not available. * Cookie __Host-psifi.analyticsTraceV2 * Duration 6 hours * Description Description is currently not available. * Cookie _gh_sess * Duration session * Description GitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form. * Cookie YSC * Duration session * Description YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages. * Cookie ajs_anonymous_id * Duration 1 year * Description This cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before. * Cookie vuid * Duration 1 year 1 month 4 days * Description Vimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. Performance Performance 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. * Cookie AWSALB * Duration 7 days * Description AWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target. * Cookie acq * Duration past * Description Description is currently not available. * Cookie acq.sig * Duration past * Description Description is currently not available. * Cookie ptc * Duration 2 years * Description No description available. Advertisement Advertisement 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. * Cookie muc_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to collect user behaviour and interaction data to optimize the website. * Cookie guest_id_marketing * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie guest_id_ads * Duration 1 year 1 month 4 days * Description Twitter sets this cookie to identify and track the website visitor. * Cookie personalization_id * Duration 1 year 1 month 4 days * Description Twitter 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. * Cookie guest_id * Duration 1 year 1 month 4 days * Description Twitter 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. * Cookie bcookie * Duration 1 year * Description LinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs. * Cookie __Secure-ROLLOUT_TOKEN * Duration 6 months * Description YouTube 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. * Cookie yt.innertube::nextId * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie yt.innertube::requests * Duration Never Expires * Description YouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen. * Cookie session_tracker * Duration session * Description This cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website. * Cookie edgebucket * Duration session * Description Reddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content. * Cookie did * Duration 1 year * Description Arbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year. Uncategorised Other uncategorised cookies are those that are being analysed and have not been classified into a category as yet. No cookies to display. Reject All Save My Preferences Accept All Toggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation [](https://towardsdatascience.com/) Publish AI, ML & data-science insights to a global community of data professionals. Sign in[Submit an Article](https://write.insightmediagroup.io) [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/) * * * [Write For TDS](https://towardsdatascience.com/submissions/) [LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) # Optimizing Your Strategies with Approaches Beyond A/B Testing An in-depth explanation in layman on optimising classic A/B testing: Epsilon-greedy, Thompson Sampling, Contextual Bandits and more [John Leung](https://towardsdatascience.com/author/johnleungtj/) July 24, 20237 min read ![](../images/000.jpg) ### An in-depth explanation in layman on optimizing classic A/B testing: Epsilon-greedy, Thompson Sampling, Contextual Bandits, and more In the world of digital marketing, the competition is fierce. Every company wants to create a marketing strategy with the highest value – increasing customer retention, enhancing customer satisfaction, or archiving other business objectives. However, there is no perfect marketing strategy that fits all customers. Instead, we can strive to figure out an improved version of our marketing strategy. That's why A/B testing comes into play. In a nutshell, A/B testing is an experiment that involves randomly splitting your audiences into two groups, and comparing two versions of a strategy to see which one performs better. It is the traditional and statistically proven method for making data-driven decisions. Learn this step by step with the interactive [Machine Learning](https://roadmap.sh/machine-learning) roadmap. ### When to use A/B Testing Imagine you and your partner have been running a toy e-shop for a while. One day, your partner raises concerns about the background color of the landing page. He believes that the current color does not effectively drive the conversion rate of visitors (i.e. % of visitors purchasing items). * Currently in use (Strategy A): Orange * Your partner's suggestion (Strategy B): Yellow You immediately identify that A/B testing is a good experimental design. Because this is relatively straightforward to determine the "winner" or "loser" based on the measurement results (i.e. the conversion rate of each strategy), showcasing which version of the website visitors find most engaging. The following figure demonstrates the example situation. ### What's the problem with A/B Testing The result of A/B testing confirms that website visitors on the yellow landing page (strategy B) have a higher conversion rate in the e-shop. As a result, you decide to roll out the yellow landing page instead to the entire visitor population. However, you soon realize that this testing approach incurs a cost: With the strictly defined start and endpoint of the A/B testing, half of the traffic is allocated to the poorer strategy A for the entire period. > The cost difference between choosing the poorer strategy A and the most ideal option (strategy B in this case) can be represented in terms of **regret**. This A/B testing can be summarised with the following characteristics: * It emphasizes the value of **exploration:** gathering sufficient information to determine whether strategy A or B is optimal throughout the whole test. * It overrides the value of **exploitation** : not striving to achieve a higher conversion rate by reducing traffic to strategy A during the test, even with the collected information that strategy B appears to perform better. After hearing the result of the A/B testing, your partner gets excited about his success in suggesting a better strategy. He is interested to know further if other background colors, including green, red, and purple, can drive the conversion rate. You can already imagine that the scenario would get worse using the current testing method, as the regret of A/B testing will likely be even higher when more strategies are involved in the test. ### Go beyond A/B Testing We welcome new strategies, as they may bring us closer to our business goals. However, we usually doubt if we waste a significant amount of our limited time and resources on poor strategies during the test. **In classic A/B testing, we remain in the exploration mode, disregarding whether a strategy seems to be outperforming or underperforming.** What if we took the opposite approach and solely focused on exploitation? Here's how it would work: The strategy that seems to be optimal is quickly chosen by our guess when only a few visitors make purchases in the e-shop, and then all visitors are directed to this strategy for the remaining time of the test. This approach carries a high level of risk since the sample size is likely not representative enough to draw the correct conclusions based on the performance of strategies. Now, we understand neither sole exploration nor sole exploitation are good ideas. We need to balance between exploration and exploitation. Besides, it is unarguably that we should continuously allocate more traffic to the current optimal strategy during the test. > In other words, we prefer taking actions that maximize the estimated **reward** in the midst. ### Epsilon-Greedy The Epsilon-Greedy algorithm works with similar ideas by randomly selecting between exploration and exploitation for each visitor's traffic. Let's say we want to have 90% of the traffic focus on exploitation and 10% on exploration. The algorithm follows these steps: When a visitor arrives at the e-shop, 1. Identify the current optimal strategy 2. There is a 90% chance of exploitation: direct the visitor to the current optimal strategy. Alternatively, there is a 10% chance of exploration: direct the visitor to one of the remaining strategies. Over time, we greedily choose among different strategies for exploitation and gradually make a consistent decision of the currently optimal strategy. **However, we don't discard the opportunity to experiment with other strategies, even coming close to the final stage of testing.** By maximizing the estimated reward in each action, the overall cost of regret with this approach is likely to be smaller than that of classic A/B testing. ### Thompson Sampling Epsilon-Greedy seems promising, but can it be further improved? Let's consider two scenarios: (A) When only a few visitors have arrived, can we confidently identify the optimal strategy for the entire population? _Probably not. We are concerned that these samples might be outliers. Since there is still high uncertainty, exploration remains necessary._ (B) When thousands of visitors have arrived or we are in the later stages of the test, do we have greater confidence now? _Most likely, because more samples provide higher statistical power, allowing us to identify the strategy that outperforms others in the real population._ Now, let's refine our approach. To choose between exploration and exploitation based on our confidence level, we can implement Thompson Sampling. **This approach selects a strategy with the highest probability of converting a visitor into a buyer, rather than focusing solely on maximizing expected rewards.** Each time a visitor comes to the e-shop, this approach tracks the total number of buyers and non-buyers for each strategy and constructs their respective beta distributions. The advantages are: * When the sample size is small, even a strategy with a lower conversion rate may be selected (prioritizing exploration). * As evidence becomes more apparent, the strategy with a higher conversion rate will be chosen most of the time (prioritizing exploitation). ### Contextual Bandits Thompson Sampling is an effective approach as it dynamically adjusts the balance between exploration and exploitation throughout the test. While it allows us to make informed decisions about which strategy to apply when a customer arrives, these decisions are not personalized for different customer cohorts. Let's consider two visitors: Peter and Mary. Peter enjoys watching Minions videos with his child every weekend, while Mary has no interest in them. Even in the early stages of the test, we can predict that Peter would prefer visiting the toy e-shop page in yellow (the main color of Minions!) and have a higher likelihood of making a purchase. This example highlights the importance of considering the visitor's context data. By utilizing Contextual Bandits, we can apply algorithmic decisions more systematically. In a real-life scenario, the context may include historical data about each customer, such as website clicks, past purchases, frequency of opening personalized emails, and even data from their ongoing session, such as recent search queries. **The algorithm can then learn to associate different contexts with the strategies that are most likely to lead to conversions for each visitor.** ### Any More Novel Ideas We have gone through various approaches, including classic A/B testing, the Epsilon-Greedy algorithm, Thompson Sampling, and Contextual Bandits. * Classic A/B testing: Involve remaining in the exploration mode. * Epsilon-Greedy algorithm: Randomly balances between exploration and exploitation. * Thompson Sampling: Place more emphasis on exploitation as the sample size for each strategy increases * Contextual Bandits: Provide personalized and optimal strategies based on the context (side information) of the visitors These approaches have been further refined and discussed in recent research papers. Here are a few examples: * ["Neural Contextual Bandits with Deep Representation and Shallow Exploration](https://openreview.net/forum?id=xnYACQquaGV)": Neural Contextual Bandits rely on the exploration which takes place over the entire network parameter space, this is typically inefficient for large-size networks. The paper suggests a new algorithm with shallow exploration and setting of a regret bound. * ["An Empirical Evaluation of Federated Contextual Bandit Algorithms](https://paperswithcode.com/paper/an-empirical-evaluation-of-federated)": This paper explores the combination of federated learning, a decentralized approach to training machine learning models, with contextual bandits. It addresses concerns related to incorporating federated learning, such as utilizing a small amount of pre-training data. * * * ### Before you go If you enjoy this reading, I invite you to **** follow [my Medium page](https://medium.com/@johnleungTJ). By doing so, you can stay updated with exciting content related to data science, project management, and self-improvement. ## Register Now to Continue Reading ### Unlock Towards Data Science with a Free Account All articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more. Register for freeSign in to your account * * * ![](../images/body-4766f66da8cde6940868.jpeg) Written By ## [John Leung](https://towardsdatascience.com/author/johnleungtj/) [See all from John Leung](https://towardsdatascience.com/author/johnleungtj/) [Ab Testing](https://towardsdatascience.com/tag/ab-testing/)[Algorithms](https://towardsdatascience.com/tag/algorithms/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Reinforcement Learning](https://towardsdatascience.com/tag/reinforcement-learning/)[Statistics](https://towardsdatascience.com/tag/statistics/) Share This Article * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Foptimizing-your-strategies-with-approaches-beyond-a-b-testing-bf11508f8930%2F&title=Optimizing%20Your%20Strategies%20with%20Approaches%20Beyond%20A%2FB%20Testing) * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Foptimizing-your-strategies-with-approaches-beyond-a-b-testing-bf11508f8930%2F&title=Optimizing%20Your%20Strategies%20with%20Approaches%20Beyond%20A%2FB%20Testing) * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Foptimizing-your-strategies-with-approaches-beyond-a-b-testing-bf11508f8930%2F&text=Optimizing%20Your%20Strategies%20with%20Approaches%20Beyond%20A%2FB%20Testing) Towards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program. [Write for TDS](https://towardsdatascience.com/submissions/) ## Related Articles * [![](../images/001.jpg)](https://towardsdatascience.com/leveraging-llama-2-features-in-real-world-applications-building-scalable-chatbots-with-fastapi-406f1cbeb935/) ## [Leveraging Llama 2 Features in Real-world Applications: Building Scalable Chatbots with FastAPI...](https://towardsdatascience.com/leveraging-llama-2-features-in-real-world-applications-building-scalable-chatbots-with-fastapi-406f1cbeb935/) [Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/) An In-Depth Exploration: Open vs Closed Source LLMs, Unpacking Llama 2's Unique Features, Mastering the Art of Prompt Engineering, and... [Luís Roque](https://towardsdatascience.com/author/luisroque/) July 24, 202316 min read * [![](../images/002.jpeg)](https://towardsdatascience.com/how-to-use-large-language-models-llm-in-your-own-domains-b4dff2d08464/) ## [How to Use Large Language Models (LLM) in Your Own Domains](https://towardsdatascience.com/how-to-use-large-language-models-llm-in-your-own-domains-b4dff2d08464/) [ChatGPT](https://towardsdatascience.com/category/artificial-intelligence/chatgpt/) Canonical and research-proven techniques to adapt large language models to domain specific tasks and the intuition of their effectiveness. [Eileen Pangu](https://towardsdatascience.com/author/eileen-code4fun/) March 20, 20239 min read * [![](../images/003.jpeg)](https://towardsdatascience.com/master-apache-airflow-how-to-install-and-setup-the-environment-in-10-minutes-61dad52d5239/) ## [Master Apache Airflow: How to Install and Setup the Environment in 10 Minutes](https://towardsdatascience.com/master-apache-airflow-how-to-install-and-setup-the-environment-in-10-minutes-61dad52d5239/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) Learn how to set up and configure the environment for writing your first DAG. [Dario Radečić](https://towardsdatascience.com/author/radecicdario/) May 11, 20216 min read * [![](../images/004.jpg)](https://towardsdatascience.com/a-framework-for-building-a-production-ready-feature-engineering-pipeline-f0b29609b20f/) ## [A Framework for Building a Production-Ready Feature Engineering Pipeline](https://towardsdatascience.com/a-framework-for-building-a-production-ready-feature-engineering-pipeline-f0b29609b20f/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) Lesson 1: Batch Serving. Feature Stores. Feature Engineering Pipelines. [Paul Iusztin](https://towardsdatascience.com/author/pauliusztin/) April 28, 202314 min read * [![](../images/005.png)](https://towardsdatascience.com/orbitron-reinventing-the-wheels-and-its-control-algorithm-8f60ffd44238/) ## [Orbitron: Reinventing the wheels and its control algorithm](https://towardsdatascience.com/orbitron-reinventing-the-wheels-and-its-control-algorithm-8f60ffd44238/) [Programming](https://towardsdatascience.com/category/programming/) Being a heavy Sci-fi fan myself, I always wondered: how would those spherical wheels from Tron and I-Robot work in real life? And this... [Tyler Kim](https://towardsdatascience.com/author/tylertaewook/) April 2, 20218 min read * [![](../images/006.png)](https://towardsdatascience.com/ml-pipelines-with-grid-search-in-scikit-learn-2539d6b53cfb/) ## [ML Pipelines with Grid Search in Scikit-Learn](https://towardsdatascience.com/ml-pipelines-with-grid-search-in-scikit-learn-2539d6b53cfb/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) ML Pipeline is an important feature provided by Scikit-Learn and Spark MLlib. It unifies data preprocessing, feature engineering and ML... [Benjamin Wang](https://towardsdatascience.com/author/benjamin-wang/) January 26, 20217 min read * [![](../images/007.jpeg)](https://towardsdatascience.com/how-to-be-an-encouraging-supervisor-for-your-data-science-intern-661fcf3baeef/) ## [How to Be an Encouraging Supervisor for Your Data Science Intern](https://towardsdatascience.com/how-to-be-an-encouraging-supervisor-for-your-data-science-intern-661fcf3baeef/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) 7 actionable steps that can benefit your team and the intern [Arunn Thevapalan](https://towardsdatascience.com/author/arunnthevapalan/) September 21, 20217 min read * [![](../images/008.png)](https://towardsdatascience.com/a-data-scientists-journey-from-sudoku-to-kaggle-120876b7fa33/) ## [A Data Scientist's journey from Sudoku to Kaggle](https://towardsdatascience.com/a-data-scientists-journey-from-sudoku-to-kaggle-120876b7fa33/) [Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/) In conversation with Rohan Rao: A Data Scientist, Kaggle Grandmaster, and India's reigning Sudoku Champion. [Parul Pandey](https://towardsdatascience.com/author/pandeyparul/) July 24, 20198 min read * [![](../images/009.jpg)](https://towardsdatascience.com/how-do-grayscale-images-affect-visual-anomaly-detection/) ## [How Do Grayscale Images Affect Visual Anomaly Detection?](https://towardsdatascience.com/how-do-grayscale-images-affect-visual-anomaly-detection/) [Computer Vision](https://towardsdatascience.com/category/computer-vision/) A practical exploration focusing on performance and speed [Aimira Baitieva](https://towardsdatascience.com/author/aimira.baitieva/) July 24, 20256 min read * [YouTube](https://www.youtube.com/c/TowardsDataScience) * [X](https://x.com/TDataScience) * [LinkedIn](https://www.linkedin.com/company/towards-data-science/) * [Threads](https://www.threads.net/@towardsdatascience) * [Bluesky](https://bsky.app/profile/towardsdatascience.com) [](https://towardsdatascience.com/) Your home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals. Subscribe 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/) © Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network) #### Recommended Articles Close * [ ![](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") * [ ![](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") * [ ![](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") * [ ![](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?")