This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
This course is primarily intended for the following participants:
Data Scientists looking to quickly go from machine learning prototype to production to deliver business impact.
Software Engineers looking to develop Machine Learning Engineering skills.
ML Engineers who want to adopt Google Cloud for their ML production projects.
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Overview
Syllabus
- Welcome to the Machine Learning Operations (MLOps): Getting Started
- This module provides the overview of the course
- Employing Machine Learning Operations
- ML practitioners’ pain points, The concept of DevOps in ML, The three phases of the ML lifecycle, Automating the ML process
- Vertex AI and MLOps on Vertex AI
- What is Vertex AI and why does a unified platform matter?, Introduction to MLOps on Vertex AI, How does Vertex AI help with the MLOps workflow? Part 1, How does Vertex AI help with the MLOps workflow? Part 2
- Summary
Taught by
Google Cloud Training
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