
Overview

Coursera Plus Annual Sale:
All Certificates & Courses 40% Off!
Grab it
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.
>>> By enrolling in this course you agree to the Qwiklabs Terms of Service as set out in the FAQ and located at: https://qwiklabs.com/terms_of_service
Syllabus
- Welcome to the Machine Learning Operations (MLOps): Getting Started
- This module provides the overview of the course
- Employing Machine Learning Operations
- This module identifies ML practitioners' pain points before exploring the concept of DevOps in ML. You're introduced to the three phases of the ML lifecycle and automating the ML process.
- Vertex AI and MLOps on Vertex AI
- This module explores what Vertex AI is and why a unified platform matters.
- Summary
Taught by
Google Cloud Training