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
Machine Learning Operations (MLOps): Getting Started
This course is part of Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
Instructor: Google Cloud Training
Access provided by Coursera Learning Team
38,814 already enrolled
Gain insight into a topic and learn the fundamentals.
4.1
(458 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 2 hours
Learn at your own pace
91%
Most learners liked this course
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
Shareable certificate
Add to your LinkedIn profile
Assessments
2 assignments
Taught in English
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This course is part of the Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Google Cloud
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There are 4 modules in this course
This module provides the overview of the course
What's included
1 video
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.
What's included
5 videos1 reading1 assignment
This module explores what Vertex AI is and why a unified platform matters.
What's included
6 videos1 reading1 assignment1 app item
Summary
What's included
1 video1 reading
Instructor
Instructor ratings
4.2 (166 ratings)Offered by
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4.1
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Showing 3 of 458
RL
4
Reviewed on Jan 6, 2022
It's ok. There are example notebooks to understand the code. The pricing part is missing.
DM
5
Reviewed on Feb 2, 2021
Thank You , Coursera & Google, It was great session & learn some practical Aspects & fundamentals of ML. I hope it will help me in the future. Thank You.
SC
5
Reviewed on Jul 7, 2021
Well designed course with Qwiklabs hands-on experience, awesome learning. Thanks to Google Cloud Team and Coursera
Recommended if you're interested in Data Science
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