​​​This workshop is for data scientists and machine learning engineers who want to learn how to move machine learning projects from prototypes and experiments to production as a repeatable process. If you want to build production-grade, SLA-satisfying software systems in Python code, including core MLOps building blocks (such as versioning, experiment tracking, workflows, and so on), then this course is for you.

​This workshop will teach you all the ingredients of full-stack machine learning and the infrastructure you’ll need to use to deploy your ML and AI models to production. It will involve continuous, hands-on coding using our Metaflow sandbox, a browser-based VSCode environment with the ML infrastructure to power Metaflow provisioned for you at the click of a button.

​You’ll learn how to:
- ​​Orchestrate machine learning workflows;
- ​​​Use versioning, model reporting, and notebooks to inspect your workflows and models;
- ​​​Leverage cloud compute resources to scale entire workflows and single steps;
- ​​Deploy workflows and models to production systems and;
- ​Configure A/B tests to establish iterative AI development cycles;
- And much more.

​This course is not a typical data science workshop in the sense that:
- This is an engineering-focused course, not an ML course. You will get to build actual functional systems, not learn about how to train models or run cross-validation. That said, the course is intended to be accessible to non-engineers: we want to make sure that all (data) scientists can build real-world DS, ML, and AI software systems;
​- Outerbounds provides managed infrastructure for the course, including data storage in S3, Kubernetes as a computing platform, workflow scheduling and event-triggering with Argo, and more - all accessed through Python files and notebooks in a familiar IDE.

All workshop correspondence will occur on Slack: please join the community Slack here and then join the channel #workshop-full-stack-ml-may-2024