This course is intended for new and seasoned practitioners of Machine Learning and Data Science with some level of experience programming in Python and working in notebooks environment.
In this course, you will build a prediction service, not just train a model. This will take you to the next step in Machine Learning (ML).
This course is built with the most easy and least technological friction possible; you will need a working knowledge of Python and a Github account.
Extra tools, frameworks and software will be provided during certain modules and will always be following the principle of being free, and sustainably accessible (not temporary accounts or limited access).
This course is intended to move beyond the classic MLOps gap where data scientists and ML practitioners create models and have difficulties using those models for real applications and services.
In this course; you will learn how to produce such prediction services, end-to-end, with state of the art technologies... and for free!
Learn to develop and operate AI-enabled (prediction) services on serverless infrastructure
Learn MLOps fundamentals: versioning, testing, data validation, and operations
Develop and run serverless feature pipelines
