This repo manages a set of labs designed to demonstrate best practices and patterns for implementing and operationalizing production grade ML workflows on Google Cloud Platform.

With a few exceptions the labs are self-contained - they don't rely on other labs. The goal is to create a portoflio of labs that can be utilized in development and delivery of scenario specific demos and workshops.

• Lab-00- Environment Setup. This lab guides you through the process of provisioning and configuring a reference MLOps environment on GCP. Most other labs rely on the environment configured in this lab. .

• Lab-01-KFP-AutoML. This lab demonstrates how to use Kubeflow Pipelines to orchestrate an ML workflow that utilizes BigQuery for feature engineering and AutoML Tables for model training and deployment.

• Lab-12-TFX-KFP. This lab walks you through the development and and deployment of a TFX pipeline that uses Dataflow and Cloud AI Platform as processing runtimes and Kubeflow Pipelines for workflow orchestration.