Online Course

Intro to MLOps

This Intro to MLOps course offers a comprehensive look into the evolving world of Machine Learning Operations (MLOps). It's designed for anyone looking to bridge the often-challenging gap between developing machine learning models and effectively deploying and managing them in real-world production environments. We'll explore the essential principles of MLOps within the industry leading Databricks ecosystem, and hone best practices that ensure your AI solutions are not only powerful but also reliable, scalable, and maintainable.

You'll gain a strong foundational understanding of how MLOps integrates with and extends core concepts from DevOps, such as automation, version control, and continuous integration/continuous delivery (CI/CD), adapting them specifically for the unique demands of machine learning workflows.

Key areas of focus include:

• DataOps - processes and frameworks focused on the data engineering element of making models a reality
• ModelOps - the core data science methods for creating & serving machine learning models
• DevOps - focuses on CI/CD, testing and reliability. Adapts many best practices from the software engineering discpline for the realities of machine learning
• LLMOps - we conclude with a section exploring how MLOps concepts apply to the new wave of generative AI
• Pratical Exercises - throughout learners are intended to be hands on with the concepts being taught

By the end of this course, you'll be equipped with the tangible skills and theoretical knowledge to design, implement, and manage robust MLOps pipelines, transforming your ability to deliver production-grade machine learning solutions. Whether you're a data scientist, machine learning engineer, or a software developer looking to move into the AI space, this course will provide you with the essential toolkit for success in the operationalization of machine learning.