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CODeepLearning.AI· on Coursera
Introduction to Machine Learning in Production
Advanced English SpecializationFREE
Our recommendation
This 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 and monitoring — the core of MLOps. Part of Andrew Ng's MLOps Specialization on Coursera, and genuinely valuable, but it is an advanced course aimed at people who already build models.
Good for: Learning MLOps — how to deploy and maintain ML systems in the real world.
Less suitable if: You are new to machine learning or have never trained a model.
Requirements: Solid ML fundamentals and Python; some deep-learning experience helps.
Realistic time: Around 12–15 hours over a few weeks.
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.
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
What it costs
Free to audit on Coursera. A certificate needs a Coursera subscription (about $49/month) or Coursera Plus, with financial aid available. It is the first course in the Machine Learning Engineering for Production (MLOps) Specialisation.
Comparison · LBS
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Same topic, different options. We surface the trade-offs others hide so you can pick the course that actually fits your time, budget, and goals.
A DeepLearning.AI certificate on Coursera is well respected in the ML community as evidence of the work, though it is a course certificate rather than a formal qualification.
Last updated
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.