Machine Learning in Google Cloud

Overview

Delivery method

Virtual classroom

Duration

5 days

Audience

Information Technology Specialists

Description

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions.

You learn how to build AutoML models without writing a single line of code; build BigQuery ML models using SQL, and build Vertex AI custom training jobs by using Keras and TensorFlow. You also explore data preprocessing techniques and feature engineering.

Learning Objectives 

• Describe the technologies, products, and tools to build an ML model, an ML pipeline, and a Generative AI project;

• Understand when to use AutoML and BigQuery ML;

• Create Vertex AI-managed datasets;

• Add features to the Vertex AI Feature Store;

• Describe Analytics Hub, Dataplex, and Data Catalog;

• Describe how to improve model performance;

• Create Vertex AI Workbench user-managed notebook, build a custom training job, and deploy it by using a Docker container;

• Describe batch and online predictions and model monitoring;

• Describe how to improve data quality and explore the data;

• Build and train supervised learning models;

• Optimize and evaluate models by using loss functions and performance metrics;

• Create repeatable and scalable train, eval, and test datasets;

• Implement ML models by using TensorFlow or Keras;

• Understand the benefits of using feature engineering;

• Explain Vertex AI Model Monitoring and Vertex AI Pipelines.

Target Audience

• Aspiring machine learning data analysts, data scientists, and data engineers;

• Learners who want exposure to ML and use Vertex AI, AutoML, BigQuery ML, Vertex AI Feature Store, Vertex AI Workbench, Dataflow, Vertex AI Vizier for hyperparameter tuning, and TensorFlow/Keras.

Prerequisite(s)

• Some familiarity with basic machine learning concepts

• Basic proficiency with a scripting language, preferably Python