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.
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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.
What You Will Learn
- Identify and use core technologies required to support effective MLOps.
- Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
- Adopt the best CI/CD practices in the context of ML systems.
- Implement reliable and repeatable training and inference workflows.
Syllabus
WEEK 1
Welcome to MLOps Fundamentals
This module provides the overview of the course
Why and When do we need MLOps
In this module, we take a look at machine learning from an operations perspective. This means taking a whole-system view: from defining the problem to the solution.
Understanding the Main Kubernetes Components (Optional)
WEEK 2
Introduction to AI Platform Pipelines
In this module, we’ll be discussing a Google Cloud product, AI Platform Pipelines, that makes MLOps easy, seamless, and scalable with Google Cloud Services.
Training, Tuning and Serving on AI Platform
In this module, we will learn how to train, tune, and serve a model manually from the Jupyter notebook on AI Platform.
WEEK 3
Kubeflow Pipelines on AI Platform
In this module, we will automate the training and tuning process we described before using a Kubeflow pipeline. Instead of having to trigger every single step of the process manually from the Jupyterlab notebook, we can trigger the entire process with a single click after we have expressed the various steps as a Kubeflow pipeline.
CI/CD for Kubeflow Pipelines on AI Platform
In this module, we will be talking about CI/CD for Kubeflow pipelines. We know how to build an automated Kubeflow pipeline, but how can we integrate this pipeline in a continuous integration stack? The goal is to rebuild pipeline assets immediately when new training code is pushed to the corresponding repository.
Summary
This module is a recap of what was covered in the course
