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DeepLearning.AI · on Coursera

Introduction to Machine Learning in Production

Advanced English SpecializationFREE
SkillsMLOpsMachine learning deploymentData driftConcept driftModel monitoringML lifecycle

Is this course right for you?

Our take
This takes on the part of machine learning the tutorials quietly skip: everything that happens after the model works once.

Good for: Learning MLOps — how to deploy and maintain ML systems in the real world.

Skip if: You are new to machine learning or have never trained a model.

It covers the full project lifecycle, data and concept drift, deployment patterns and monitoring — the substance of MLOps, and the difference between a notebook that runs and a system that keeps working in production. That value comes with a prerequisite, though: it's an advanced course from Andrew Ng's MLOps Specialisation aimed at people who already build models, so if you're new to ML or have never trained one, it's too soon.

It's the first course in that specialisation, free to audit on Coursera, with the DeepLearning.AI certificate on a subscription or Coursera Plus (financial aid available). The MLOps practices it teaches are current and hold up well (as of 2026).

Comparison · LBS

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About this course

Introduction to Machine Learning in Production tackles the part of ML that tutorials skip: everything after the model works once. It covers the machine-learning project lifecycle, data and concept drift, deployment patterns, monitoring, and how to scope an ML system so it keeps working as the world changes around it.

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Instructor

AN
Andrew Ng
Coursera instructor

From Andrew Ng and DeepLearning.AI's MLOps specialization, with deep input from practitioners who run ML systems at scale. The emphasis is engineering discipline and production realities rather than model theory.

Frequently asked questions

No — beginners to production ML, perhaps, but not to machine learning itself. It assumes you already understand ML fundamentals (training models, evaluation) and focuses on the challenge of deploying and maintaining models in the real world. It is the first course of a specialization aimed at people moving from building models to running them in production, so prior ML knowledge is genuinely needed.

MLOps is the practice of reliably deploying, monitoring, and maintaining machine-learning models in production — the operational side of ML, akin to DevOps for software. It covers issues that only appear once a model is live: data changing over time, monitoring performance, retraining, and managing the whole lifecycle. This course introduces that discipline, which is where many ML projects actually succeed or fail.

A working knowledge of machine learning and comfortable Python. You should already understand how models are trained and evaluated, since this focuses on getting them into production rather than teaching ML from scratch. Andrew Ng's Machine Learning or Deep Learning courses are natural prerequisites. Without that ML foundation, the production-focused material will lack the context it builds on.

Yes. It is the first course in DeepLearning.AI's Machine Learning Engineering for Production (MLOps) specialization, which covers deploying and maintaining ML systems end to end. You can take this introduction alone for a grasp of what production ML involves, but it is designed as the opening of that sequence, with the later courses going deeper into pipelines, data, and deployment.

Yes, you can audit it on Coursera and watch the lectures at no cost. The graded assignments and the certificate require a subscription, with financial aid available if you apply. Since much of this introductory course is conceptual, auditing captures a good deal of the value if you want to understand MLOps before committing to the fuller, more hands-on specialization.
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