MLOps (Machine Learning Operations) Fundamentals
Offered By: Pluralsight
Course Description
Overview
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.
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.
Syllabus
- 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
Taught by
Google Cloud
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