Overview

This three-week hands-on course provides a jumpstart into design and development of modern MLOps platforms. From initial project scoping to exploratory analysis and data engineering, from feature engineering to model training, to ML pipelines and CI/CD automation, productization, monitoring and operations.

You are going to create an ML project in Amazon SageMaker Studio and go through all stages of development such as data exploration, interactive experimentation, setting up MLOps pipelines, and finally deliver the project into production. You’ll learn how to work with a feature store, model registry, pipelines, model and data monitor, and CI/CD projects.

Through this course, you will work with Amazon SageMaker to get you inspired to implement your own MLOps solution. The course provides recommended patterns and practical architecture blueprints for real-world ML projects. The course gives you an overview of the industry-standard ML platforms and MLOps products, like MLflow, Apache Airflow, H20, DataIKU.

As the course main deliverable, you’re required to implement an end-to-end ML project with the main components of MLOps, such as reproducible ML pipelines, scalable data processing and model training, model registry, experiment tracking, observability, and event-driven workflows.