Dive into the foundations of Machine Learning Operations (MLOps), learning the core concepts of productionizing and monitoring machine learning models to add business value! This skill track covers the complete life-cycle of a machine learning application, ranging from the gathering of business requirements to the design, development, deployment, operation, and maintenance stages.
This track includes conceptual courses designed to help you expand your data science knowledge into the world of production machine learning software!
Upon completion of the track, you'll be equipped with an MLOps mindset and will be deeply familiar with concepts such as CI/CD and CM/CT, experiment tracking, model registries, feature stores, and different deployment strategies. Additionally, you'll gain an understanding of critical elements of operating machine learning applications in production, such as data drift and model drift.
Overview
Syllabus
- MLOps Concepts
- Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.
- Developing Machine Learning Models for Production
- Shift to an MLOps mindset, enabling you to train, document, maintain, and scale your machine learning models to their fullest potential.
- MLOps Deployment and Life Cycling
- In this course, you’ll explore the modern MLOps framework, exploring the lifecycle and deployment of machine learning models.
- Fully Automated MLOps
- Learn about MLOps architecture, CI/CD/CM/CT techniques, and automation patterns to deploy ML systems that can deliver value over time.
Taught by
Folkert Stijnman, Nemanja Radojković, Arturo Opsetmoen Amador, and Sinan Ozdemir
