Full-Stack Machine Learning with Metaflow
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.
What this Workshop Covers
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
Among other things!
Warnings!
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.