{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Kurtis Pykes",
  "title_listed": "A Simple Explanation of MLOps",
  "keywords": "MLOps",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/a-simple-explanation-of-mlops-e47009e245f7/",
  "publish_date": "2021-08",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "74a19833cb8e",
  "collected_at": "2026-10-02T00:26:57",
  "title": "A Simple Explanation of MLOps | Towards Data Science",
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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# A Simple Explanation of MLOps\nAnd The Challenges Involved\n[Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\nAugust 8, 20214 min read\n![Photo by National Cancer Institute on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/08/0NCBMSA3SdHBDyuR7-scaled.jpg)\nPhoto by [National Cancer Institute](https://unsplash.com/@nci?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\n**Also shared at Datatron Blog:**[**What is MLOps?**](https://blog.datatron.com/what-is-mlops/)\nFinally! The company you've been working for, for the past 8 years has heeded your cries to invest in machine learning - a growing feat poised to revolutionize business as we know it. You were placed in charge of growing the Data Science team, but it's no biggie, you're foresight has you covered.\nThe past 18 months of lingering around lucrative Data Science circles and becoming acquainted with the culture in your free time unlocked you privileged access into some of the most competent Data Science circles around. Excited by the prospects of churning out models to solve problems that were out of reach before the rise of Artificial intelligence, you lay down some demo projects to get things running.\n6 weeks later the results come back and performance metrics on the demos are blossoming. Former internal Artificial Intelligence skeptics convert to AI evangelists overnight. When the executives are informed, their pupils begin to dilate until they look like dollar signs. \"Jackpot baby!\" one executive screeched, \"We need this in production ASAP. How soon can you do it?\" asked another. Considering all the complicated tasks that have been solved, one would imagine that we could be swimming in the dollars pretty quickly. All that was left to do was some routine development work, Right? Wrong.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [Computer Science](https://roadmap.sh/computer-science) and [Product Manager](https://roadmap.sh/product-manager) roadmaps.\nTo put this into perspective, at the end of 2019 going into 2020, a report shared in DeepLearning.AI's newsletter, _The Batch,_ stated that \"_only 22 percent of companies using machine learning have successfully deployed a model, the study found._ \"\n> \"_More and more companies are developing machine learning models for internal use. But many are still struggling to bridge the gap to practical deployments._ \"\n> (**Source** : DeepLearning.AI, The Batch Newsletter)\nUnderstanding the challenges we are confronted by when using Machine Learning in Development provides the foundation for what MLOps does and why it is essential for companies adopting Machine Learning into their workflow.\n## What are the Challenges?\nWe may begin by first becoming acquainted with the term \"DevOps\". DevOps constitutes a set of best practices in software engineering that has made it possible to ship software into a production environment within minutes whilst ensuring that the application is running reliably whilst in production. Therefore it may be said that DevOps is a software engineering practice that unifies software DEVelopment and software OPerationS.\n**DevOps = Software Development + Software Operations**\nHere is where the issue lies. When building traditional software applications, much of the concern for the DevOps team is regarding the code. On the other hand, Machine Learning applications involve code as well as data - effectively, the fundamental difference between a Machine Learning application and a Software Engineering application.\nThe final machine learning model that is deployed into production encompasses an algorithm that has been applied to a large set of data (better known as the training data) which in effect determines the behavior of the model in a production environment - the model's behavior is also dependent on the input data that it receives at inference time, but we have no way of knowing in advance what it would be.\n![Image By Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/08/1Bt1uj6tJ-u1fSR5JMqhvaw.png)\nImage By Author\nEssentially, code in both traditional software applications and machine learning applications is crafted in a controlled development environment. However, machine learning applications also include data that comes from a never-ending source called the \"real-world\". Data does not stop changing, and there is no measure to completely control how data should change. To enhance your perception of this concept, you may consider a relationship between code and data of which both live in their own independent planes though sharing a dimension of time.\n![Image By Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/08/1Dw2Fc3zIR0valP_zC7HAOg.png)\nImage By Author\nThe gap between the planes is illustrative of a disconnect that is the root of several vital challenges that ought to be overcome by absolutely anybody attempting to deploy a Machine Learning model into production successfully. These challenges include:\n  * Slow deployments\n  * Training-Serving skew (a difference between performance during training and performance during serving)\n  * Lack of reproducibility\n\n\nThe goal of the person attempting to deploy machine learning models into production is to bridge the gap between the 2 planes, in turn overcoming challenges such as those listed above.\n### Final Thought\nTo summarise, MLOps covers the spectrum of best practices that go into deploying and maintaining machine learning systems in production, efficiently and reliably. Machine Learning introduces its own specific challenges when working in production. Thus, MLOps as a discipline was birthed out of DevOps but accounted for all of the intricacies involved in machine learning that aren't readily available from a traditional DevOps workflow.\n### Related Articles\n> [**Model Drift in Machine Learning**](https://towardsdatascience.com/model-drift-in-machine-learning-8023e3d08217)\n> [**Machine Learning Model Deployment**](https://medium.datadriveninvestor.com/machine-learning-model-deployment-b1eaf7ca96cd)\n> [**Building Reproducible Machine Learning Pipelines**](https://towardsdatascience.com/building-reproducible-machine-learning-pipelines-c80407c4319f)\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. 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