Machine Learning with TensorFlow in Vertex AI

Overview

In this lab you create a Vertex AI Workbench instance on which you devlop a TensorFlow model in Jupyter notebook. You train the model, create an input data pipeline, deploy it to an endpoint, and get predictions.

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

Vertex AI brings AutoML and AI Platform together into a unified API, client library, and user interface. With Vertex AI, both AutoML training and custom training are available options.

Vertex AI Workbench helps users quickly build end-to-end notebook-based workflows through deep integration with data services (like Dataproc, Dataflow, BigQuery, and Dataplex) and Vertex AI. It enables data scientists to connect to Google Cloud data services, analyze datasets, experiment with different modeling techniques, deploy trained models into production, and manage MLOps through the model lifecycle.

Vertex AI Workbench is a single development environment for the entire data science workflow.

This lab uses a set of code samples and scripts developed for Data Science on the Google Cloud Platform, 2nd Edition from O'Reilly Media, Inc.

Objectives

Deploy Vertex AI Workbench instance

Create minimal training, validation data

Create the input data pipeline

Create TensorFlow model

Deploy model to Vertex AI

Deploy Explainable AI model to Vertex AI

Make predictions from the model endpoint