The Best MLOps Courses in 2026

This list ranks courses on four criteria:

• Conceptual depth: whether the course explains the full MLOps lifecycle, or just shows one deployment script and calls it done
• Hands-on rigor: whether you build, deploy, and monitor a real pipeline, not just watch a diagram
• Platform and tooling coverage: whether the course sticks to one cloud or framework, or gives you a feel for how the pieces (MLflow, model registries, CI/CD, cloud ML services) fit together
• Instructor expertise and outcomes: who's teaching it and what you can actually operate afterward

Most courses on this list can be started for free or on a trial; a few require a paid platform subscription, one-time purchase, or Nanodegree tuition for full access.

1. MLOps Concepts — DataCamp

DataCamp's MLOps Concepts is the best course on this list for developers and technical leads who want a clear map of the MLOps lifecycle before they touch a single deployment tool.

• Level: Intermediate
• Time: ~2 hours, self-paced
• Cost: Free to start; full access included with a DataCamp subscription (~$12.42/month)
• Best for: Developers and technical leads who want a focused, conceptual grounding in MLOps before committing to a longer, tool-heavy program

The course moves through the core phases of the machine learning lifecycle — design and development, deployment, and maintenance — covering feature stores, experiment tracking, containerization, CI/CD, and the statistical and computational monitoring that triggers retraining.

2. Machine Learning DevOps Engineer Nanodegree — Udacity

Udacity's Machine Learning DevOps Engineer Nanodegree is a great option for developers who want a project-based program that treats MLOps as its own discipline rather than an afterthought bolted onto a modeling course.

• Level: Advanced
• Time: ~4 months at 10 hours/week
• Cost: Nanodegree tuition
• Best for: Developers who already know the data science workflow and want to build production-grade Python code, CI/CD pipelines, and monitoring on top of it

The program covers clean, testable, production-ready Python code, then moves into building a reusable ML pipeline with experiment tracking and data versioning, before finishing with model deployment via FastAPI and a full CI/CD setup. Each of the four projects is reviewed by Udacity's reviewer network before you can pass.

3. Machine Learning in Production — DeepLearning.AI (Coursera)

Andrew Ng's Machine Learning in Production course is a great option for developers who want a rigorous, single-course grounding in the theory of running ML systems at scale, from one of the field's most recognized instructors.

• Level: Intermediate
• Time: ~10 hours across 3 modules
• Cost: Free to audit; Coursera subscription for the certificate
• Best for: Developers who want to understand the reasoning behind deployment patterns, concept drift, and data-centric AI before they start operating a system themselves

The course covers the ML project lifecycle and deployment patterns, then moves into modeling challenges like error analysis and skewed datasets, and closes with data definition, label consistency, and establishing a performance baseline. It rates 4.8/5 across more than 3,300 reviews.

4. MLOps | Machine Learning Operations Specialization — Duke University (Coursera)

Duke University's MLOps Specialization, taught by Noah Gift and Alfredo Deza, is a great option for developers who want breadth across multiple cloud platforms rather than depth on a single vendor's tooling.

• Level: Advanced
• Time: Four-course specialization, roughly 6 months at 5 hours/week
• Cost: Free to audit individual courses; Coursera subscription for the certificate
• Best for: Developers who need to operate ML systems across more than one environment — AWS SageMaker, Azure ML, and open-source tooling — rather than standardize on just one

The specialization moves through Python essentials for MLOps, then into DevOps and DataOps practices for CI/CD and reproducibility, before covering SageMaker and Azure ML deployments and closing with MLflow and Hugging Face tooling for experiment tracking and model registries.

5. Complete MLOps Bootcamp With 10+ End-to-End ML Projects — Udemy

The Udemy MLOps Bootcamp is a good option for developers who want maximum hands-on project variety covering data science and MLOps together.

• Level: Intermediate to Advanced
• Time: Bootcamp-style, 10+ end-to-end projects
• Cost: One-time Udemy purchase (frequently discounted)
• Best for: Developers who want practice with the infrastructure side — Docker, MLflow, CI/CD — as much as the modeling side

The course covers building and automating deployment, monitoring, and scaling for ML models using MLflow, Docker, and modern MLOps frameworks, then works through more than ten real-world projects covering the full path from data ingestion to a running production pipeline.

6. MLOps Zoomcamp — DataTalks.Club

MLOps Zoomcamp is a good option for developers who want a completely free, community-supported course with a structured weekly cadence.

• Level: Intermediate (assumes Python, Docker basics, and prior ML experience)
• Time: 9 weeks, self-paced
• Cost: Free
• Best for: Developers who want a free, structured path with homework, a leaderboard, and a Slack community for troubleshooting, rather than a solo video course

The course covers experiment tracking and model management, workflow orchestration, model deployment, and monitoring, and closes with a final project reviewed by peers. All materials, videos, and homework are open and free, though live cohort support runs only periodically.

7. MLOps (Machine Learning Operations) Fundamentals — Pluralsight

Pluralsight's MLOps Fundamentals course is a good option for developers already working in the Google Cloud ecosystem who want a focused look at one platform's MLOps tooling.

• Level: Intermediate
• Time: Short-form course
• Cost: Pluralsight subscription
• Best for: Developers who want to see MLOps tools and best practices applied specifically to deploying, evaluating, and monitoring models on Google Cloud

The course introduces the deployment, testing, monitoring, and automation practices that make up MLOps, applied throughout to Google Cloud's own tooling for continuous evaluation of deployed models.

Best MLOps Courses Comparison Table