Most companies struggle with machine learning operations. Models get trained, but turning them into reliable, monitored, production systems often falls on ML engineers, data scientists, or whoever “owns” the model—usually without clear processes or tooling. As a result, teams reinvent workflows, rely on ad-hoc scripts, and operate ML systems with limited visibility and no reproducibility.
MLOps Zoomcamp is a free MLOps course designed to close that gap. It teaches you how to put ML models into production using real tools: MLflow, orchestration frameworks, deployment patterns, monitoring, and AWS examples, without drowning you in theory. By the end, you’ll know how to build, deploy, and maintain a complete ML pipeline the way modern teams expect.
MLOps Zoomcamp course curriculum showing 6 modules from infrastructure to production deployment
If you’re an ML engineer, data scientist, or software developer working with ML models and want to elevate your MLOps skills, MLOps Zoomcamp provides a practical, end-to-end foundation you can apply immediately.
Table of Contents
- What is MLOps Zoomcamp?
- Why is MLOps Important?
- Who is This Course For?
- Course Curriculum
- How MLOps Zoomcamp Works
- What is DataTalks.Club Community?
- How to Join MLOps Zoomcamp
- Frequently Asked Questions
What is MLOps Zoomcamp?
MLOps Zoomcamp is a free MLOps course that takes you from experiment tracking to production deployment in 6 modules plus a portfolio project.
You’ll learn infrastructure setup with Docker and AWS, experiment tracking with MLflow, pipeline orchestration with Mage, model deployment (batch, real-time, and streaming), monitoring with Prometheus and Evidently AI, and testing/CI/CD best practices.
The course culminates in a real-world project where you build, deploy, and monitor a complete ML pipeline that you can showcase to employers.
Why is MLOps Important?
MLOps bridges the gap between model development and production deployment by turning machine learning experiments into reliable, continuously running services. Unlike traditional software, ML systems introduce challenges such as data drift, model degradation, reproducibility issues, and the need for ongoing retraining. These characteristics make MLOps a standalone discipline with its own workflows, tools, and operational requirements.
Yet, MLOps is still a relatively new field and many organizations struggle with it. In practitioner surveys, 84.3% of data scientists and ML engineers report that detecting and diagnosing production model issues is a recurring problem; 26.2% say it takes one week or more to resolve an issue. Without proper MLOps practices, organizations may spend millions on ML models that don’t deliver or can’t be sustained.
As machine learning becomes a strategic capability, MLOps is the engine that makes it reliable, maintainable, and scalable. This recognition is driving rapid market growth:
- The global MLOps market was valued at approximately US $2.19 billion in 2024 and is projected to reach around US $16.6 billion by 2030, growing at a compound annual growth rate (CAGR) of ~40.5%.
- Another estimate puts the global MLOps market at US $1.7 billion in 2024, with a forecast CAGR of 37.4% through 2034.
Who is This Course For?
This course is for practitioners who work with machine learning models and want to learn how to operationalize them—from experimentation to deployment, monitoring, and ongoing maintenance. It’s especially relevant for data scientists, ML engineers, and software engineers who are expected to manage the full lifecycle of ML systems.
If you’re comfortable with the command line and Python, and you have prior exposure to machine learning and Docker (either from work or from courses like the ML Zoomcamp), you have the right background to follow the MLOps Zoomcamp successfully.
Course Curriculum
Course overview: a complete journey through modern MLOps tools and technologies
The curriculum follows a logical progression from experimentation to production deployment and monitoring, culminating in an end-to-end project. Here’s what you’ll learn each week:
| Module | Topic | Focus | Tools You'll Use |
|---|---|---|---|
| 1 | Infrastructure & Prerequisites | Build your dev environment with Docker, AWS, and containerized deployment basics | Docker, AWS, Terraform, cloud shells |
| 2 | Experiment Tracking & Model Management | Track experiments, manage model versions, and compare runs | MLflow Tracking, MLflow Model Registry |
| 3 | Orchestration & ML Pipelines | Create reproducible pipelines and manage dependencies end-to-end | Mage, Airflow, Prefect |
| 4 | Model Deployment | Ship models via batch jobs, web APIs, and streaming services | Flask, Docker, AWS Lambda, AWS Kinesis |
| 5 | Model Monitoring | Detect drift and monitor health with production-grade dashboards | Prometheus, Grafana, Evidently AI |
| 6 | Testing & CI/CD | Add testing, CI/CD, and cloud infrastructure fundamentals | Pytest, GitHub Actions, LocalStack |
What You’ll Build: Course Project
The final part of the course is dedicated to a hands-on MLOps project where you apply everything you’ve learned to build a real, production-ready ML system. This project becomes a strong addition to your portfolio and demonstrates that you understand not only how to train models but also how to operationalize them.
You’ll:
- Choose a dataset that interests you
- Train a model and track experiments using MLflow or Weights & Biases
- Build an automated training pipeline using tools like Mage, Airflow, or Prefect
- Deploy your model as a batch job, web service, or streaming system
- Set up monitoring with Evidently AI, Prometheus, or Grafana
- Implement CI/CD workflows using GitHub Actions or GitLab CI/CD
- Document your architecture and make the project reproducible
You have freedom to choose your stack: cloud providers (AWS, GCP, Azure), orchestration tools, experiment trackers, and monitoring systems, while still following the core MLOps lifecycle required by the course.
Completing the project and reviewing peers’ work is also what qualifies you for the MLOps Zoomcamp certificate.
How MLOps Zoomcamp Works
GitHub Repository: Your Source of Truth
All lessons, homework, and cohort updates live in the MLOps Zoomcamp GitHub repository.
MLOps Zoomcamp GitHub repository showing course materials and structure
Video Lectures
Lectures are pre-recorded and available on the official YouTube playlist, so you can follow the live cadence or binge-watch at your own pace.
Homework Assignments
We release homework assignments for each week of the course. Your scores are added to an anonymous leaderboard, creating friendly competition among course members and motivating you to do your best.
Course leaderboard displaying student progress and homework scores anonymously
Learning in Public
A unique feature is our “learning in public” approach, inspired by Shawn @swyx Wang’s article. We believe that everyone has something valuable to contribute, regardless of their expertise level.
Extract from Shawn @swyx Wang's article explaining the benefits of learning in public
Throughout the course, we actively encourage and incentivize learning in public. By sharing your progress, insights, and projects online, you earn additional points for your homework and projects.
Course leaderboard highlighting bonus points earned through learning in public activities
This not only demonstrates your knowledge but also builds a portfolio of valuable content. Sharing your work online also helps you get noticed by social media algorithms, reaching a broader audience and creating opportunities to connect with individuals and organizations you may not have encountered otherwise.
How to Get a Certificate
To receive a certificate, you’ll need to complete the final project and peer review 3 other students’ projects:
- Complete the final project: Build a real-world MLOps project that demonstrates your mastery of all course concepts
- Peer review: Evaluate and provide feedback on 3 fellow students’ projects during the peer review process
- Submit on time: Meet the project submission deadlines to qualify for certification
What is DataTalks.Club Community?
Active discussions and support in the MLOps Zoomcamp Slack community channel
DataTalks.Club is a global community of 80,000+ data professionals who connect on Slack to share knowledge, ask career questions, and collaborate across analytics, ML, and data engineering. When you join MLOps Zoomcamp, the dedicated Slack channel becomes your daily workspace for troubleshooting, accountability, and celebrating wins with peers following the same modules.
How to Join MLOps Zoomcamp
You can join MLOps Zoomcamp either by following a live cohort or learning at your own pace.
All materials are freely available in the MLOps Zoomcamp GitHub repository. Each module has its own folder, and cohort-specific homework and deadlines are in the cohorts directory. Lectures are pre-recorded and available in this YouTube playlist.
Option 1: Self-Paced Learning
Start anytime. You get full access to materials and community support on Slack.
Complete homework assignments: homework and solutions are available on the course platform. Build a project for your portfolio.
Under self-paced learning, homework isn’t scored, your project isn’t peer-reviewed, and you can’t earn a certificate.
Option 2: Live Cohort
Runs once per year (typically starts in the spring).
Includes:
- Updated homework
- Automatic homework scoring and a leaderboard
- Project peer review
- Eligibility for a certificate after meeting all requirements
Even if you join after the official start date, you can still follow along — but note that some homework forms may already be closed. All active deadlines are listed on the course platform.
To earn a certificate, you’ll need enough time to complete one final project and the required peer reviews. Details are in the Projects and Certificate sections.
Frequently Asked Questions
The MLOps Zoomcamp is a free, community-driven program by DataTalks.Club that teaches core MLOps skills through hands-on project work.
This 3-month course covers a comprehensive curriculum with all materials open and available anytime on GitHub. You’ll work with an industry-standard stack including MLflow, Docker, AWS, Prometheus, Grafana, Mage, and GitHub Actions and earn a certificate.
“Zoomcamp” is a term that originated from Alexey Grigorev, the founder of DataTalks.Club. It started with his book “ML Bookcamp.” When Alexey decided to create a video course based on the book, he called it “Machine Learning Zoomcamp” - a free, cohort-based course in video format. The name “zoomcamp” is a play on “bookcamp,” referring to the video format of the course. The Zoomcamp series has since expanded to include other free courses like the Data Engineering Zoomcamp, MLOps Zoomcamp, and LLM Zoomcamp, all following the same community-driven, open-source philosophy.
Yes, the MLOps Zoomcamp is completely free. There are no hidden costs, no tuition fees, and no paid tiers. All course materials, videos, homework assignments, and access to the community are provided at no cost. Unlike traditional bootcamps that charge $10,000-$20,000+, this course is entirely community-driven and open source.
The MLOps Zoomcamp differs from traditional MLOps bootcamps in several key ways:
- Cost: Completely free vs. $10,000-$20,000+ for bootcamps
- Community: Community-driven and open source with all materials available forever on GitHub vs. content locked behind paywalls
- Flexibility: Can continue at your own pace after the cohort ends vs. rigid schedules and limited access periods
To earn a certificate, you need to complete one end-to-end MLOps project that demonstrates your mastery of all course concepts. After submitting your project, you must also review at least 3 other students’ projects by the deadline and provide constructive feedback.
The next cohort of the MLOps Zoomcamp typically starts in the spring each year. Register here: https://airtable.com/appYdhA23GVZd1iN2/shrCb8y6eTbPKwSTL before the course starts.
The MLOps Zoomcamp is run by DataTalks.Club, a global online community of data professionals and learners. While the initial idea and most of the content were created by Alexey Grigorev, members of the DataTalks.Club community contribute as instructors and maintainers. Expert instructors include Cristian Martinez, Emeli Dral, and others.
DataTalks.Club is often referred to as “the DataTalks Club”, “data talks club”, or “datatalks club”.
To get the most out of this MLOps course, you should have prior programming experience (1+ year), basic understanding of machine learning concepts, familiarity with Python, basic command line knowledge, and previous exposure to Docker (recommended). Prior exposure to machine learning from work or other courses (e.g., from ML Zoomcamp) is helpful.
Expect to spend 5-15 hours per week, depending on your background. This includes watching videos, completing homework, and working on the final project. More time may be needed during the final project weeks.
Yes! All course materials, videos, and recordings remain available after the cohort ends, and you can learn at your own pace. You’ll have access to the Slack community for support. However, self-paced learning does not include homework submissions, project evaluations, or the ability to earn a certificate. To receive a certificate, you need to join an active cohort.
You have multiple support channels available. Join the DataTalks.Club Slack community where you can ask questions and get help from instructors and fellow students. We also have an FAQ repository with answers to common questions and a @ZoomcampQABot in Slack for quick help.
The GitHub repository is https://github.com/DataTalksClub/mlops-zoomcamp.
Course videos are available in the YouTube playlist. For easier navigation, refer to the GitHub repository. We also maintain year-specific playlists for updates.
There are no office hours—all lectures are pre-recorded and available in the YouTube playlist, so you can watch them whenever it suits you.
All course materials are in the GitHub repository. Each module has its own folder (for example, 01-intro or 03-classification), while cohort-specific homework and deadlines are located in cohorts/2025.
Occasionally, additional workshops or updated implementation videos are released—there will be additional announcements if this happens.
You need to complete one end-to-end MLOps project to earn a certificate. The project is a comprehensive MLOps solution that demonstrates your mastery of all course concepts including experiment tracking, pipeline orchestration, model deployment, and monitoring. After submitting your project, you’ll also need to review at least 3 other students’ projects. Learn more about the final project and certificate requirements.
MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning, DevOps, and Data Engineering. It enables automating ML pipelines, deploying models to production, monitoring model performance, and ensuring ML system reliability. Learning MLOps is essential for anyone working with machine learning in production environments.
The course covers essential MLOps tools and platforms including MLflow for experiment tracking, Docker for containerization, AWS services (including Kinesis), Prometheus and Grafana for monitoring, Mage for ML pipeline orchestration, and GitHub Actions for CI/CD.
MLOps experiment tracking is a core component of the course. You’ll learn to use MLflow for comprehensive experiment tracking, including tracking model parameters, metrics, and artifacts. The course covers implementing model registry and versioning, organizing ML experiments effectively, and comparing experiment results. This hands-on training ensures you can manage and reproduce ML experiments in production environments.
While this course does not provide an official MLflow certification from the MLflow creators, you’ll receive comprehensive MLflow training as part of the MLOps Zoomcamp. The course includes in-depth coverage of MLflow for experiment tracking, model registry, and versioning. Upon completing the course with a live cohort, you’ll earn a DataTalks.Club MLOps Zoomcamp certificate that demonstrates your proficiency with MLflow and other MLOps tools. This practical training is more valuable than a standalone certification as it’s integrated into a complete production ML workflow.
The DataTalks.Club MLOps community is a supportive network of 80,000+ data professionals and learners. As part of the MLOps Zoomcamp, you’ll have access to a dedicated course channel in Slack where you can ask questions, get help from instructors and peers, share your progress, and connect with like-minded individuals. The community provides technical support, peer learning opportunities, and networking that can lead to collaborations and career opportunities. This active community is one of the key differentiators of the MLOps Zoomcamp experience.
This comprehensive training covers the complete machine learning operations lifecycle. You’ll receive hands-on training in experiment tracking with MLflow, containerization with Docker, ML pipeline orchestration with Mage, model deployment (batch, real-time, and streaming), monitoring with Prometheus and Grafana, and CI/CD with GitHub Actions. The course includes 6 core technical modules, weekly homework assignments, and a final end-to-end project. This practical training prepares you for real-world production ML systems and is taught by expert instructors including Cristian Martinez, Alexey Grigorev, and Emeli Dral.
Yes! This is a completely free MLOps course, with a certificate available when you complete the course with a live cohort. There are no hidden costs or tuition fees. To earn your certificate, you’ll need to complete the technical modules, build one end-to-end MLOps project, participate in peer reviews, and follow MLOps best practices. This free course provides the same quality training as paid bootcamps but at no cost. Certificates, homework submissions, and project evaluations are only available when participating in a live cohort, not in self-paced mode.
Yes, certificates are available when completing the course with a live cohort. Requirements include completing the technical modules, building one final end-to-end MLOps project, participating in peer reviews, and following MLOps best practices. Note that certificates, homework submissions, and project evaluations are not available in self-paced mode.