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.
Machine Learning Operations (MLOps): Getting Started
This course is part of Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
Taught in English
Instructor: Google Cloud Training
Sponsored by University of Virginia
35,059 already enrolled
Course
(419 reviews)
92%
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What you'll learn
Identify and use core technologies required to support effective MLOps.
Adopt the best CI/CD practices in the context of ML systems.
Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
Implement reliable and repeatable training and inference workflows.
Details to know
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2 quizzes
Course
(419 reviews)
92%
Recommended experience
See how employees at top companies are mastering in-demand skills
Build your Machine Learning expertise
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- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
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There are 4 modules in this course
This module provides the overview of the course
What's included
1 video
ML practitioners’ pain points, The concept of DevOps in ML, The three phases of the ML lifecycle, Automating the ML process
What's included
5 videos1 reading1 quiz
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
What's included
6 videos1 reading1 quiz1 app item
Summary
What's included
1 video1 reading
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