Machine Learning Operations (MLOps): Getting Started
Machine Learning Operations (MLOps): Getting Started
These skills were generated by A.I. Do you agree this course teaches these skills?
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.
Course Info
Objectives
- 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.
Prerequisites
Completed Machine Learning with Google Cloud or have equivalent experience
Audience
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.
Available languages
English, français, 한국어, português (Brasil), español (Latinoamérica) и 日本語