Booking no:
36447
Machine learning projects quickly become technically complex and require the right approach, governance and infrastructure to be sustainable, efficient and scalable. Using the recognized MLOps framework, this course provides a helpful guide with best practices, methods and tools to optimally manage and map the lifecycle of machine learning models. You will get to know all MLOps stages - from data versioning to monitoring - in detail.
1. MLOps - what it is and why you can't do without it
2. data versioning and experiment tracking
3. data pipeline orchestration
4. experiment tracking
5. CI/CD for machine learning
6. deployment and serving
7. monitoring
8 MLOps in the cloud
9. machine learning platforms
10. excursus: LLMOps
Technical requirements
In this training , you will learn how to use the methods and tools in many practical exercises. You will carry these out yourself locally on your own computer. To make this possible, we ask you to install and test the Docker Desktop and Git applications before the course starts - please note that you will need the appropriate permissions on your computer. After installation, you can download and set up the packages provided by the trainers. You will receive detailed instructions about two weeks before the course date.
