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Axalon Training
T-GCPML-IOfficial Google Curriculum

Machine Learning on Google Cloud

5 daysILTIntermediateLoading...

Overview

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.

What You'll Learn

  • 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

Who Should Attend

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

Prerequisites

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

Products Covered

Vertex AIAutoMLBigQuery MLVertex AI PipelinesTensorFlowModel GardenGenerative AI StudioLLM APIsNatural Language APIVertex AI WorkbenchVertex AI Feature StoreVizierDataplexAnalytics HubData CatalogVertex AI TensorBoardDataflowDataprep

Course Modules

1

Introduction to AI and Machine Learning on Google Cloud

Topics

  • 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

Learning Outcomes

  • 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 LabsModule QuizzesModule Readings
2

Launching into Machine Learning

Topics

  • 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

Learning Outcomes

  • 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 LabsModule QuizzesModule Readings
3

TensorFlow on Google Cloud

Topics

  • 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

Learning Outcomes

  • 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 LabsModule QuizzesModule Readings
4

Feature Engineering

Topics

  • 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

Learning Outcomes

  • 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 LabsModule QuizzesModule Readings
5

Machine Learning in the Enterprise

Topics

  • 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

Learning Outcomes

  • 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 LabsModule QuizzesModule Readings

What's Not Covered

  • Cloud concepts and fundamentals
  • networking
  • security

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Course Details

Course Code
T-GCPML-I
Duration
5 days
Format
ILT
Level
Intermediate
Modules
5
Activities
18
Price
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