MLOps (Machine Learning Operations) Fundamentals
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This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
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.
Author Name: Google Cloud
Author Description:
Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.
Table of Contents
- Welcome to MLOps Fundamentals
1min - Why and When do we Need MLOps
15mins - Understanding the Main Kubernetes Components (Optional)
105mins - Introduction to AI Platform Pipelines
41mins - Training, Tuning and Serving on AI Platform
82mins - Kubeflow Pipelines on AI Platform
71mins - CI/CD for Kubeflow Pipelines on AI Platform
13mins - Summary
1min - Course Resources
0mins
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