Our MLOps Zoomcamp course
- Sign up here: https://airtable.com/shrCb8y6eTbPKwSTL
- Register in DataTalks.Club's Slack
- Join the
#course-mlops-zoomcampchannel - Tweet about the course!
- Start watching course videos! Course playlist
- Technical FAQ
- For announcements, join our Telegram channel
- Start: 13 May 2024 (Monday) at 17:00 CET
- Registration link: https://airtable.com/shrCb8y6eTbPKwSTL
- Subscribe to our public Google Calendar (it works from Desktop only)
All the materials of the course are freely available, so that you can take the course at your own pace
- Follow the suggested syllabus (see below) week by week
- You don't need to fill in the registration form. Just start watching the videos and join Slack
- Check FAQ if you have problems
- If you can't find a solution to your problem in FAQ, ask for help in Slack
Teach practical aspects of productionizing ML services — from training and experimenting to model deployment and monitoring.
Data scientists and ML engineers. Also software engineers and data engineers interested in learning about putting ML in production.
- Python
- Docker
- Being comfortable with command line
- Prior exposure to machine learning (at work or from other courses, e.g. from ML Zoomcamp)
- Prior programming experience (at least 1+ year)
The best way to get support is to use DataTalks.Club's Slack. Join the #course-mlops-zoomcamp channel.
To make discussions in Slack more organized:
- Follow these recommendations when asking for help
- Read the DataTalks.Club community guidelines
We encourage Learning in Public
- What is MLOps
- MLOps maturity model
- Running example: NY Taxi trips dataset
- Why do we need MLOps
- Course overview
- Environment preparation
- Homework
- Experiment tracking intro
- Getting started with MLflow
- Experiment tracking with MLflow
- Saving and loading models with MLflow
- Model registry
- MLflow in practice
- Homework
- Workflow orchestration
- Mage
- Three ways of model deployment: Online (web and streaming) and offline (batch)
- Web service: model deployment with Flask
- Streaming: consuming events with AWS Kinesis and Lambda
- Batch: scoring data offline
- Homework
- Monitoring ML-based services
- Monitoring web services with Prometheus, Evidently, and Grafana
- Monitoring batch jobs with Prefect, MongoDB, and Evidently
- Testing: unit, integration
- Python: linting and formatting
- Pre-commit hooks and makefiles
- CI/CD (GitHub Actions)
- Infrastructure as code (Terraform)
- Homework
- End-to-end project with all the things above
- Cristian Martinez
- Tommy Dang
- Alexey Grigorev
- Emeli Dral
- Sejal Vaidya
- Machine Learning Zoomcamp - free 4-month course about ML Engineering
- Data Engineering Zoomcamp - free 9-week course about Data Engineering
I want to start preparing for the course. What can I do?
If you haven't used Flask or Docker
- Check Module 5 from ML Zoomcamp
- The section about Docker from Data Engineering Zoomcamp could also be useful
If you have no previous experience with ML
- Check Module 1 from ML Zoomcamp for an overview
- Module 3 will also be helpful if you want to learn Scikit-Learn (we'll use it in this course)
- We'll also use XGBoost. You don't have to know it well, but if you want to learn more about it, refer to module 6 of ML Zoomcamp
I registered but haven't received an invite link. Is it normal?
Yes, we haven't automated it. You'll get a mail from us eventually, don't worry.
If you want to make sure you don't miss anything:
- Register in our Slack and join the
#course-mlops-zoomcampchannel - Subscribe to our YouTube channel
Thanks to the course sponsors for making it possible to run this course
Do you want to support our course and our community? Reach out to [email protected]