Make your machine learning models into battle-ready services with Seldon Core Foundation, a comprehensive 2-day course designed for data and tech professionals. Participants gain an in-depth understanding of Seldon Core’s architecture, deployment, and usage as a toolkit for deploying machine learning workflows.
Day one opens with a Seldon Core overview, then walks through deploying and configuring the platform hands-on. Students explore Model Servers and Language Wrappers, learning how each serves a vast array of machine learning frameworks without custom serving code. They finish the day by deploying a model into a running pipeline.
Day two moves into production concerns, starting with service orchestration for chaining models and pre- and post-processing steps. Attendees configure metadata, metrics, and tracing for observability, and apply routing strategies such as canary and A/B deployments. They also survey the broader integrations and ecosystem that surround the platform. Participants leave with hands-on experience configuring and managing its components and building machine learning models into production-ready processes. They gain the expertise to elevate their machine learning workflows using some of the most advanced, scalable tools available.
Who Should Attend
Developers, Data Scientists, Data Engineers, Machine Learning Engineers, MLOps professionals
What Attendees Will Learn
Upon completing Seldon Core Foundation, participants will be able to:
- Understand how to build the core of an MLOps pipeline using Seldon Core
- Build hands-on experience installing, deploying and configuring Seldon Core
- Become familiar with the core components and architecture of a Seldon Core pipeline
- Learn how to use Model Servers and Language Wrappers to serve a vast array of machine learning solutions
- Gain exposure to the integrations and ecosystem of tools surrounding Seldon Core
- 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.