CODeepLearning.AI · on Coursera
Machine Learning Engineering for Production (MLOps) Specialization
Advanced 160 hours English Specialization Certificate Certificate
About this course
The MLOps Specialization bridges the gap between training models and running them in production. Across four courses, you design ML pipelines, manage data and model versioning, deploy with TFX, and monitor for data and concept drift.
Andrew Ng co-created this specialization to address the most common enterprise ML failure mode — great models that never ship or degrade silently after launch.
What you'll learn
Design end-to-end ML pipelines with data validation and feature engineering
Version datasets and models using ML metadata stores
Deploy models with TensorFlow Serving and build prediction APIs
Monitor production models for data and concept drift
Apply CI/CD practices to ML workflows with automated retraining
This course includes
160h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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Instructor
AN
Andrew Ng / Laurence Moroney / Robert Crowe
Coursera instructor
180K+ learners6 courses4.7 instructor rating
Created by DeepLearning.AI with Google practitioners. Robert Crowe leads the TFX-focused sections.
Requirements
- ML fundamentals; Python and basic TensorFlow or Keras experience
Who this course is for
- ML engineers and data scientists moving models into production
- Software engineers building ML-powered products
- Teams struggling with ML reproducibility and deployment reliability
About this provider
CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
Frequently asked questions
TFX is primary, but concepts translate directly to Kubeflow, MLflow, and other pipeline tools.
Yes — Google Cloud is used for examples, but patterns apply to AWS and Azure.