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 <<<
Introduction to MLOps-Why and when to employ MLOps
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Reviews
4 (461 ratings)
- 5 stars48.80%
- 4 stars25.16%
- 3 stars14.09%
- 2 stars4.55%
- 1 star7.37%
TK
Feb 20, 2021
This was a good course along with google qwiklab which guide you through out the lab which makes a enrolled person a successful learner .
SO
Nov 13, 2020
Great course to start for learning about MLOps. However, I hope there will have more videos to explain details on LABs.
From the lesson
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.
Taught By
Google Cloud Training