Machine Learning on Google Cloud

• 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.

This class is primarily intended for the following participants:

• 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

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

Module 1: Introduction to AI and Machine Learning on Google Cloud

Objectives

• 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.

Activities

• Hands-On Labs
• Module Quizzes
• Module Readings

Module 2: Launching into Machine Learning

Objectives

• 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.

Activities

• Hands-On Labs
• Module Quizzes
• Module Readings

Module 3: TensorFlow on Google Cloud

Objectives

• 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.

Activities

• Hands-On Labs
• Module Quizzes
• Module Readings

Module 4: Feature Engineering

Objectives

• 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.

Activities

• Hands-On Labs
• Module Quizzes
• Module Readings

Module 5: Machine Learning in the Enterprise

Objectives

• 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.

Activities

• Hands-On Labs
• Module Quizzes
• Module Readings