MLOps Zoomcamp is a free 3-month bootcamp that covers everything from basic MLOps concepts to advanced model deployment and monitoring.
It is perfect for people who plan to work with ML services at any stage.
What You’ll Learn in This Guide
- Key course features
- Who is the course for?
- Course curriculum
- Course project for your portfolio
- Course assignments
- Learning in public
- DataTalks.Club community
Why is MLOps Important?
MLOps (Machine Learning Operations) is becoming a critical skill in the AI and machine learning industry. As organizations move from experimental ML projects to production systems, understanding the complete MLOps cycle is essential for:
- Streamlining ML development: Automate and standardize the machine learning lifecycle
- Ensuring production success: Bridge the gap between data science and operations
- Maintaining quality: Implement MLOps best practices for reliable ML systems
- Scaling ML solutions: Build robust ML pipelines that can handle real-world demands
This free MLOps course provides hands-on training with industry-standard tools like MLflow, Docker, and AWS. Whether you’re interested in MLOps training, looking for an MLOps bootcamp, or want to join an active MLOps community, our comprehensive program covers everything you need to succeed in machine learning operations.
What Makes MLOps Zoomcamp Different
- Comprehensive Curriculum: The course explores each part of the entire MLOps cycle
- Hands-On Project: A final project to apply the skills learned from the course and enhance your portfolio
- Diverse materials: Video lectures, code samples, and community notes. Weekly homework for practice.
- Supportive community: Course channel in Slack to ask questions and interact with peers and instructors.
- Expert Instructors: Cristian Martinez, Alexey Grigorev, Emeli Dral, and others.
Who the Course is For and Prerequisites
This course is for:
- Data scientists
- ML engineers
- Software developers who are interested in understanding MLOps, the process of putting machine learning code in production.
MLOps involves transitioning the raw code from a development environment into a deployed model within a live service, including stages for performance monitoring and problem-solving.
Here are the main prerequisites for the course:
- Prior programming experience (at least 1+ year)
-
Prior exposure to machine learning (at work or from other courses, e.g. from ML Zoomcamp)
- Being comfortable with the command line
- Python
- Docker (you can check ML Zoomcamp for that)
Course Curriculum
The course curriculum is structured to guide you through the essential elements of MLOps, beginning with foundational concepts and advancing to production deployment and monitoring.
Course overview: A complete journey through modern MLOps tools and technologies
Course Structure
The curriculum follows a logical progression from experimentation to production deployment, culminating in an end-to-end project. Here’s what you’ll learn each week:
Core Technologies
Module 1: Infrastructure & Prerequisites
- Set up development environment with Docker and cloud services
- Learn AWS basics and infrastructure setup
- Hands-on practice with containerization and ML model deployment basics
Module 2: Experiment Tracking and Model Management
- Master experiment tracking with MLflow
- Implement model registry and versioning
- Track and organize ML experiments effectively
- Compare and analyze experiment results
Module 3: Orchestration and ML Pipelines
- Build automated ML training pipelines with Mage
- Implement reproducible model training workflows
- Create end-to-end ML pipeline orchestration
- Manage dependencies and data flow
Module 4: Model Deployment
- Deploy models using multiple approaches:
- Batch prediction pipelines
- Real-time web services with Flask
- Streaming services with AWS Lambda & Kinesis
- Learn containerization for model serving
- Implement CI/CD for ML models
Module 5: Model Monitoring
- Set up monitoring systems with Prometheus and Grafana
- Track model performance and data drift with Evidently AI
- Implement alerting and dashboard creation
- Monitor service health and model metrics
Module 6: Best Practices
- Implement testing strategies for ML systems
- Write unit tests and integration tests
- Set up CI/CD pipelines with GitHub Actions
- Apply code quality checks and best practices
- Work with cloud infrastructure using LocalStack
Project Phase
The final weeks are dedicated to applying your knowledge in a real-world MLOps project. This project will become a valuable addition to your portfolio and demonstrate your practical MLOps skills.
- Project Requirements
- Choose a dataset that interests you
- Train a model and track your experiments
- Create an automated training pipeline
- Deploy your model (batch, web service, or streaming)
- Set up monitoring and performance tracking
- Follow MLOps best practices
- Technology Options
- Cloud: AWS, GCP, Azure
- Experiment tracking: MLflow, Weights & Biases
- Orchestration: Mage, Airflow, Prefect
- Monitoring: Evidently AI, Prometheus, Grafana
- CI/CD: GitHub Actions, GitLab CI/CD
- Evaluation Process
- Develop your project over 2 weeks
- Review at least 3 other projects during peer review week
- Provide constructive feedback to your peers
- Document your solution and architecture
Course Assignments and Scoring
Homework and Getting Feedback
To reinforce your learning, you can submit a homework assignment at the end of each week. Your scores are added to an anonymous leaderboard, creating friendly competition among course members and motivating you to do your best.
For support, we have an FAQ section with quick answers to common questions. If you need more help, our Slack community is always available for technical questions, clarifications, or guidance. Additionally, we host live Q&A sessions called “office hours” where you can interact with instructors and get immediate answers to your questions.
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.
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.
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.
Building Your Portfolio with MLOps Projects
If you’ve been in machine learning interviews or researched MLOps roles, you know that having practical experience with production ML systems is crucial. This is especially important if you’re transitioning from data science to MLOps or seeking your first role in the field.
In the final weeks of the course, you’ll build an end-to-end MLOps project that showcases everything you’ve learned. Your project will be peer-reviewed by other participants, providing you with valuable feedback and the opportunity to learn from different approaches to MLOps challenges.
DataTalks.Club Community
DataTalks.Club has a supportive community of like-minded individuals in our Slack. It is the perfect place to enhance your skills, deepen your knowledge, and connect with peers who share your passion. These connections can lead to lasting friendships, potential collaborations in future projects, and exciting career prospects.
Quick Start Guide for MLOps Zoomcamp
MLOps Zoomcamp offers a practical path to mastering machine learning operations. In just 3 months, you’ll gain:
- Hands-on experience with modern MLOps stack (MLflow, Docker, AWS, Evidently AI, Prometheus)
- Build production-ready ML systems from experimentation to monitoring
- Join a vibrant community of ML practitioners and MLOps enthusiasts
Frequently Asked Questions
What is MLOps and why should I learn it?
MLOps (Machine Learning Operations) is a set of practices that combines Machine Learning, DevOps, and Data Engineering. It's crucial for 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.Is this MLOps course really free?
Yes! This is a completely free MLOps course. You get full access to all course materials, hands-on projects and assignments, community support in our Slack workspace, and a certificate upon completion if you participate in a live cohort.How long does the course take to complete?
The course takes approximately 3 months to complete, with content spread across different modules covering the complete MLOps cycle. The course includes 6 core technical modules, project work, and a peer review period.What tools and technologies will I learn?
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.Do I need to register for the course?
Registration is not mandatory - it's primarily used to gauge interest and for analytics. You can start learning and submitting homework without registering while a cohort is "live", join the course even after it has started, and submit homework as long as the submission forms are open. However, be aware that there are deadlines for final projects, so plan accordingly.How is the course delivered?
The course includes pre-recorded video lectures you can watch at your own pace, regular office hours (live Q&A sessions) which are also recorded, course materials available in the GitHub repository, and active MLOps community support in Slack.What are the prerequisites?
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).Can I get a certificate?
Yes, certificates are available when completing the course with a "live" cohort. Requirements include completing the technical modules, building an end-to-end MLOps project, participating in peer reviews, and following MLOps best practices. Note that certificates are not available in self-paced mode.Quick Links
Ready to begin your data engineering journey? Here’s everything you need: