CODeepLearning.AI · on Coursera
Introduction to Machine Learning in Production
Advanced English Professional CertificateFREE
What you'll learn
Map the full machine-learning project lifecycle
Scope an ML system around a real-world objective
Detect and handle data drift and concept drift
Choose appropriate deployment patterns for a model
Set up monitoring so failures are caught early
Reason about the gap between offline metrics and live performance
This course includes
Yes
Certificate
Yes
Mobile access
English
Language
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Instructor
AN
Andrew Ng
Coursera instructor
— learners— courses — instructor rating
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.
Requirements
- Working knowledge of machine learning
- Comfortable with Python and basic model training
Who this course is for
- ML engineers moving toward production systems
- Data scientists who want their models actually deployed
- Engineers exploring MLOps roles
About this provider
CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
4.6 trust score
Frequently asked questions
No — it's more advanced. It assumes you already know how to train models and focuses on running them in production. Start with a foundational ML course first if you're new.
MLOps is the practice of deploying and maintaining ML systems reliably — yes, this course is a strong introduction to it (lifecycle, drift, monitoring, deployment).
You can audit it free on Coursera. The certificate and graded work require a subscription.
No, but you do need ML fundamentals. The course is designed to give you the production mindset you may not get from model-building courses alone.
You'll work through the concepts and patterns of deployment and monitoring; the focus is on building the right mental model for production ML decisions.