Metaflow Foundation is a comprehensive two-day course that turns machine learning models into battle-ready services for data and tech professionals. Day one opens with an overview of the framework. Participants then install and deploy it themselves, and move into loading and storing the data a flow depends on. From there, attendees build their first working flows, connecting the steps that move raw data through transformation and into a trained model. The opening overview also introduces the core components and architecture participants return to throughout both days, including how steps, artifacts, and runs relate to one another.
Day two shifts to running Metaflow in practice. Participants manage the dependencies each flow needs to execute reliably, then use Cards to visualize results so a pipeline’s output is easy to inspect and share with teammates. The class also looks at running flows on Kubernetes, giving pipelines the compute and scheduling a production cluster provides. It finishes with orchestration techniques that chain flows together into repeatable, end-to-end workflows. Participants leave with the skills to bring these advanced, scalable tools into their own machine learning work.
Who Should Attend
Developers, Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals
What Attendees Will Learn
Upon completing Metaflow Foundation, participants will be able to:
- Learn how to build the core of an MLOps pipeline using Metaflow
- Develop hands-on experience installing, deploying and configuring Metaflow Core
- Understand the core components and architecture of Metaflow
- Learn how to use Flows and Cards to build, observe and serve machine learning models
- Develop the skills and expertise to transform machine learning workflows into production grade end-to-end solutions
Prerequisites
Participants should have intermediate programming skills (preferably in Python), an understanding of mathematics and statistics, basic familiarity with machine learning, a grasp of computer science fundamentals, familiarity with common operating systems and basic command-line operations, and a willingness to review pre-course materials.
These prerequisites will help participants engage effectively with the course material and hands-on labs, making the learning experience more rewarding.