Best MLOps Courses Online in 2026: Top 10 for All Levels
MLOps has quietly become one of the most requested skills on 2026 engineering job listings, and for a simple reason: plenty of teams can train a model, but far fewer can keep one running reliably in production. That gap between a notebook that works on your laptop and a service that serves predictions at scale, versioned, monitored, and retrainable, is exactly what MLOps covers, and it is where the salaries have moved.
After reviewing the current catalog across the major platforms, we narrowed the field to ten courses that actually teach the production side rather than rehashing model training. The list spans university specializations, structured skill tracks, and hands-on Udemy bootcamps, so you can match the format to how you learn and how much time you have. If you are weighing the career path itself, our guide on how to become an AI platform engineer pairs well with the training below, as does our roundup of the best machine learning courses if your modeling foundations still need work.
A quick word on method: among the courses we evaluated, we weighted production depth first (CI/CD, deployment, monitoring, and retraining), then hands-on projects, then the credibility of the credential. Some of the links below are affiliate links, meaning we may earn a commission if you enroll at no additional cost to you. That never affects the ranking; the free option below sits high on the list because it deserves to.
Quick Picks: The Best MLOps Courses
| # | Course | Best For |
|---|---|---|
| 1 | MLOps | Machine Learning Operations by Duke (Coursera) | Best Overall |
| 2 | Machine Learning Engineering for Production by DeepLearning.AI (Coursera) | Best from Andrew Ng |
| 3 | MLOps Fundamentals (DataCamp) | Best Structured Track |
| 4 | MLOps Zero to Hero (Udemy) | Best Hands-On Project Course |
| 5 | MLflow for Kubernetes (Udemy) | Best for Deployment at Scale |
| 6 | Ultimate DevOps to MLOps Bootcamp (Udemy) | Best for DevOps Engineers |
| 7 | DevOps, DataOps, MLOps by Duke (Coursera) | Best Single-Course Intro |
| 8 | MLOps Deployment and Life Cycling (DataCamp) | Best Deployment Deep Dive |
| 9 | AI System Design and MLOps on AWS (Udemy) | Best for Cloud Deployment |
| 10 | Made With ML (Free) | Best Free Option |
Taking more than one Coursera pick? Three of the courses below sit on Coursera. Coursera Plus bundles all of them under a single monthly subscription, which usually works out cheaper than enrolling in each Specialization separately.
1. MLOps | Machine Learning Operations by Duke (Coursera)
Platform: Coursera (Duke University) | Level: Intermediate | Duration: ~6 months at 5 hrs/week | Certificate: Yes | Cost: Free to audit, or Coursera Plus
Duke built the most complete university path we reviewed. Across four courses it moves from Python foundations through DevOps and DataOps practice into the tools that define the field: SageMaker, Azure ML, MLflow, and the Hugging Face toolchain. It is genuinely end to end, which is rare, and it does not assume you already think like an infrastructure engineer.
The time commitment is real, roughly six months at a steady pace, but you finish with a working command of the full MLOps lifecycle rather than a narrow slice of it. For most people who want one program that leaves few gaps, this is the pick, and the Duke name reads well on a resume.
- Best for: Learners who want one thorough, credential-backed path across the whole MLOps lifecycle.
2. Machine Learning Engineering for Production (MLOps) by DeepLearning.AI (Coursera)
Platform: Coursera (DeepLearning.AI) | Level: Intermediate | Duration: ~4 months | Certificate: Yes | Cost: Free to audit, or Coursera Plus
Andrew Ng and the DeepLearning.AI team focus this four-course specialization on the conceptual backbone of production ML: managing the data lifecycle, building modeling pipelines, and deploying models that keep working as the world shifts underneath them. It is stronger on principles and system design than on any single vendor toolchain.
That framing is its edge. Tools change every year; the reasoning about concept drift, pipeline design, and deployment strategy does not. Pair it with a hands-on Udemy course below and you cover both the why and the how without much overlap.
- Best for: Learners who want the durable concepts and system-design thinking behind production ML.
3. MLOps Fundamentals (DataCamp)
Platform: DataCamp | Level: Beginner to Intermediate | Duration: ~14 hours | Certificate: Statement of Accomplishment | Cost: DataCamp subscription
DataCamp packages the essentials into four interactive courses: MLOps Concepts, developing models for production, deployment and life cycling, and fully automated MLOps. In about fourteen hours you cover CI/CD, experiment tracking, model registries, feature stores, and the common deployment strategies, all in the browser-based format that makes the material stick.
It is the most efficient way on this list to build a working MLOps mental model without a large time investment. The exercises keep you doing rather than watching, which suits anyone who learns by touching the keyboard.
- Best for: Busy learners who want a fast, structured, hands-on grounding in the fundamentals.
4. MLOps Zero to Hero (Udemy)
Platform: Udemy | Level: Beginner to Intermediate | Duration: ~10 to 15 hours | Certificate: Yes (completion) | Cost: Paid, often discounted
This is the practical project course we would point most self-learners to first. It walks through the real problems that appear after a model is trained: versioning data with DVC, tracking experiments with MLflow, containerizing with Docker, and deploying on Kubernetes using KServe. Everything is done by building, not describing.
If you learn best by shipping something end to end and then understanding why each piece exists, the pacing here works well. Lifetime access means you can return to it as your own stack evolves.
- Best for: Self-learners who want to build a full MLOps pipeline hands-on from day one.
5. MLflow for Kubernetes: Deploy and Manage ML Models at Scale (Udemy)
Platform: Udemy | Level: Intermediate | Duration: ~6 to 9 hours | Certificate: Yes (completion) | Cost: Paid, often discounted
A tighter, deployment-focused course for the specific and valuable skill of getting models from experiment to scalable service. It centers on MLflow, Kubernetes, Docker, and KServe, and it stays close to the production plumbing rather than wandering back into model training.
If your gap is the last mile, serving and scaling, rather than the modeling, this is the most direct fix on the list. It assumes some comfort with containers, so it is a better second course than a first.
- Best for: Engineers who specifically need to deploy and scale models on Kubernetes.
6. Ultimate DevOps to MLOps Bootcamp (Udemy)
Platform: Udemy | Level: Intermediate | Duration: ~12 to 16 hours | Certificate: Yes (completion) | Cost: Paid, often discounted
Built for DevOps and infrastructure professionals crossing into MLOps, this bootcamp takes a real regression use case from data processing through to production deployment on Kubernetes, building the CI/CD pipeline along the way. It speaks the language of people who already know containers and pipelines and just need the ML-specific pieces.
If you come from a DevOps background, this is the fastest bridge on the list. It skips the beginner framing and gets straight to how model workflows differ from ordinary application deployment.
- Best for: DevOps engineers who want to add ML pipelines to a skill set they already have.
7. DevOps, DataOps, MLOps by Duke (Coursera)
Platform: Coursera (Duke University) | Level: Beginner to Intermediate | Duration: ~1 month | Certificate: Yes | Cost: Free to audit, or Coursera Plus
This is a single course pulled from the wider Duke specialization, and it works as a standalone introduction to how the three operational disciplines connect. It covers the practices and tooling that tie data workflows, model workflows, and deployment together without demanding the six-month commitment of the full program.
Take it as a low-cost way to test whether MLOps is a direction you want to pursue. If it clicks, the full Duke specialization is the natural next step, and your progress carries over.
- Best for: Anyone who wants a short, credible taste of MLOps before committing to a full program.
8. MLOps Deployment and Life Cycling (DataCamp)
Platform: DataCamp | Level: Intermediate | Duration: ~4 hours | Certificate: Statement of Accomplishment | Cost: DataCamp subscription
A focused single course from DataCamp on the modern MLOps framework: the model lifecycle, deployment patterns, and writing ML code that minimizes technical debt. It also introduces the monitoring tools you need once a model is live, which is the part beginners most often skip.
It is short and pointed, best used to shore up the deployment and monitoring end after you have the fundamentals. If you already took the DataCamp track above, treat this as reinforcement rather than new ground.
- Best for: Learners who want a quick, targeted deep dive on deployment and lifecycle management.
9. AI System Design and MLOps: From Raw Data to AWS Kubernetes (Udemy)
Platform: Udemy | Level: Intermediate to Advanced | Duration: ~8 to 12 hours | Certificate: Yes (completion) | Cost: Paid, often discounted
For anyone whose stack lives on AWS, this course builds an end-to-end system from raw data to cloud deployment, using MLflow and DVC for tracking and versioning, Docker for containerization, and AWS EKS for scalable serving. It is the most cloud-specific option on the list and the most complete on system design.
The AWS focus is both its strength and its limit: hugely useful if you deploy on Amazon, less so if your team is committed to GCP or Azure. Choose it when your target environment matches.
- Best for: Engineers deploying ML systems specifically on AWS who want end-to-end coverage.
10. Made With ML (Free)
Platform: Made With ML (Goku Mohandas) | Level: Intermediate | Duration: Self-paced | Certificate: No formal certificate | Cost: Free
Made With ML is a widely respected, genuinely free course that combines machine learning foundations with software engineering and MLOps practice: testing, CI/CD, and production design, taught through a coherent project rather than disconnected lessons. The code is open and the material is kept current.
It will not hand you a branded certificate, but the depth rivals paid programs, and for many engineers it is the single best free resource available. Start here to gauge how much paid training you actually need before spending anything.
- Best for: Self-directed engineers who want production-grade MLOps depth at no cost.
How to Choose the Right MLOps Course
Start with your starting point. If you are already a software or DevOps engineer, a hands-on Udemy bootcamp or the DevOps-to-MLOps bridge will get you productive fastest, because you can skip the general infrastructure fundamentals. If you are coming from data science or modeling, a structured path like the Duke or DeepLearning.AI specialization fills the engineering gaps in the right order.
Next, decide between depth and speed. A university specialization gives you range and a recognized credential but asks for months of your time. A DataCamp track or a focused Udemy course gets you a working skill in days or weeks, which may be all you need if you are adding MLOps to an existing role rather than switching into it.
Finally, match the toolchain to your reality. Courses vary in whether they lean on AWS, Azure, GCP, or an open-source stack of MLflow and Kubernetes. Pick the program whose tools mirror what your team already runs, or plans to, and the learning transfers directly to your work instead of sitting in a certificate you never apply. When in doubt, start free with Made With ML and let it tell you where your gaps really are.
Frequently Asked Questions
Do I need to know machine learning before learning MLOps?
You need a working understanding of how models are trained and evaluated, but you do not need to be an expert. MLOps is about operating models in production, so the emphasis is on engineering, deployment, and monitoring rather than algorithm design. If your modeling foundation is thin, take an introductory machine learning course first, then layer MLOps on top.
What is the best free MLOps course?
Made With ML is the strongest free option and rivals many paid programs in depth, covering software engineering and production practice through a real project. Beyond that, most Coursera courses on this list, including the Duke and DeepLearning.AI specializations, can be audited for free; you only pay if you want the certificate.
How long does it take to learn MLOps?
It depends on your background. A DevOps or software engineer can become productive with a focused bootcamp in a few weeks. Someone building from a data science base should expect one to three months across a structured specialization to cover deployment, CI/CD, monitoring, and retraining thoroughly.
Are MLOps certificates worth it for getting a job?
A certificate from Duke or DeepLearning.AI adds credibility, especially when you are moving into the field, but employers care most about whether you can ship and maintain a production pipeline. Use the certificate to earn the interview, then let a project you have deployed, ideally on GitHub, do the real convincing.
MLOps or DevOps: which should I learn first?
If you have neither, learn core DevOps concepts first, since MLOps builds directly on CI/CD, containerization, and infrastructure automation. If you already work in DevOps, you can move straight into MLOps and focus on the ML-specific additions: data versioning, experiment tracking, model monitoring, and retraining.
Related Articles
- How to Become an AI Platform Engineer (MLOps)
- Best Machine Learning Courses Online
- Best Data Engineering Courses
If you want a single recommendation: start free with Made With ML to find your gaps, then commit to the Duke MLOps Specialization for a complete, credential-backed path. Engineers who already live in pipelines can skip straight to a hands-on Udemy course and be deploying models within the week.
