Machine Learning on Google Cloud Training

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. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project. 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.

Products

• Vertex AI
• AutoML
• BigQuery ML
• Vertex AI Pipelines
• TensorFlow
• Model Garden
• Generative AI Studio
• Large language model (LLM) APIs
• Natural Language API
• Vertex AI Workbench
• Vertex AI Feature Store
• Vizier
• Dataplex
• Analytics Hub
• Data Catalog
• TensorFlow
• Vertex AI TensorBoard
• Dataflow
• Dataprep
• Vertex AI Pipelines

Prerequisites

To get the most out of this course, participants should have:

• Some familiarity with basic machine learning concepts
• Basic proficiency with a scripting language, preferably Python

Target Audience

This course is intended for the following:

• Aspiring ML data scientists and engineers
• Data scientists, ML developers, ML engineers, data engineers, data analysts
• Google and partner field personnel who work with customers in those job roles

• 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 your 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.

Introduction to AI and Machine Learning on Google Cloud

• Recognize the AI/ML framework on Google Cloud.
• Identify the major components of Google Cloud infrastructure.
• Define the data and ML products on Google Cloud and how they support the data-to-AI lifecycle.
• Build an ML model with BigQueryML to bring data to AI.
• Define different options to build an ML model on Google Cloud.
• Recognize the primary features and applicable situations of pre-trained APIs, AutoML, and custom training.
• Use the Natural Language API to analyze text.
• Define the workflow of building an ML model.
• Describe MLOps and workflow automation on Google Cloud.
• Build an ML model from end-to-end by using AutoML on Vertex AI.
• Define generative AI and large language models.
• Use generative AI capabilities in AI development.
• Recognize the AI solutions and the embedded generative AI features.
• Hands-On Labs
• Module Quizzes
• Module Readings

Launching into Machine Learning

• Describe how to improve data quality.
• Perform exploratory data analysis.
• Build and train supervised learning models.
• Describe AutoML and how to build, train, and deploy an ML model without writing a single line of code.
• Describe BigQuery ML and its benefits.
• Optimize and evaluate models by using loss functions and performance metrics.
• Mitigate common problems that arise in machine learning.
• Create repeatable and scalable training, evaluation, and test datasets.
• Hands-On Labs
• Module Quizzes
• Module Readings

TensorFlow on Google Cloud

• Create TensorFlow and Keras machine learning models.
• Describe the TensorFlow main components.
• Use the tf.data library to manipulate data and large datasets.
• Build a ML model that uses tf.keras preprocessing layers.
• Use the Keras Sequential and Functional APIs for simple and advanced model creation.
• Train, deploy, and productionalize ML models at scale with the Vertex AI Training Service.
• Hands-On Labs
• Module Quizzes
• Module Readings

Feature Engineering

• Describe Vertex AI Feature Store.
• Compare the key required aspects of a good feature.
• Use tf.keras.preprocessing utilities for working with image data, text data, and sequence data.
• Perform feature engineering by using BigQuery ML, Keras, and TensorFlow.
• Hands-On Labs
• Module Quizzes
• Module Readings

Machine Learning in the Enterprise

• Understand the tools required for data management and governance.
• Describe the best approach for data preprocessing: From providing an overview of Dataflow and Dataprep to using SQL for preprocessing tasks.
• Explain how AutoML, BigQuery ML, and custom training differ and when to use a particular framework.
• Describe hyperparameter tuning by using Vertex AI Vizier to improve model performance.
• Explain prediction and model monitoring and how Vertex AI can be used to manage ML models.
• Describe the benefits of Vertex AI Pipelines.
• Describe best practices for model deployment and serving, model monitoring, Vertex AI Pipelines, and artifact organization.
• Hands-On Labs
• Module Quizzes
• Module Readings