Machine Learning on Google Cloud (MLGC) – Outline

Detailed Course Outline

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 datato-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