CODeepLearning.AI · on Coursera
Machine Learning Engineering for Production (MLOps) Specialization
Advanced 160 hours English Specialization
Our recommendation
DeepLearning.AI's MLOps specialisation, on taking machine-learning models into production — data pipelines, deployment, monitoring and maintaining models in the real world. It is advanced, aimed at people who already build ML models and want the engineering side. One of the better structured MLOps programmes.
Skills you'll gain
MLOpsML pipelinesModel deploymentMonitoringProduction MLData engineering
Is this course right for you?
A good fit if you…
You already build ML models
You want the production and engineering side
You work towards ML engineering
Consider something else if you…
You are new to machine learning
You only want to build models
You want a short course
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.
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
What it costs
Free to audit on Coursera; the certificate needs a subscription (about $49 a month), with financial aid available.
Comparison · LBS
Compare alternatives for Machine Learning Engineering for Production (MLOps) Specialization
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.
COCoursera4.7(12,000)
Machine Learning Engineering for Production (MLOps) Specialization
- Price
- PaidSubscription-based, free to audit
- Duration
- 160 hrs
- Level
- Advanced
- Certificate
- Specialization
COCoursera
Introduction to Machine Learning in Production
- Price
- FreeAudit free · Certificate available
- Duration
- —
- Level
- Advanced
- Certificate
- Specialization
PLPluralsight3.7(34)
Data Engineering with AWS Machine Learning
- Price
- PaidPluralsight subscription · from $21/mo billed annually (free trial)
- Duration
- 2.9 hrs
- Level
- Intermediate
- Certificate
DADataCamp4.5(4,000)
Data Engineer in Python Career Track
- Price
- PaidDataCamp subscription
- Duration
- 60 hrs
- Level
- Intermediate
- Certificate
- Specialization
Prices & availability can change — confirm on the provider's site. We're not affiliated with any single provider.
Is the certificate recognised?
Issued through Coursera with DeepLearning.AI. The production skills you build matter most.
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.
About this provider
CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
Frequently asked questions
Machine-learning and Python experience. It is advanced, focused on the engineering side, not a first ML course.
Yes for learning to take models into production — pipelines, deployment and monitoring — if you already build ML models.
You can audit it free on Coursera; the certificate needs a subscription, with financial aid available.
Building models is the data-science side; MLOps is the engineering side — deploying, monitoring and maintaining them reliably in production.
About 160 hours across the specialisation — a few months part-time.