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Machine Learning on Google Cloud

SS Course: 57557

Course 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.                                                                  

Scheduled Classes

05/13/24 - TDV - Virtual-Instructor Led - Virtual-Instructor Led (click to enroll)

What You'll Learn

Describe the technologies, products, and tools to build an ML model, an ML pipeline, and a Generative AI project.
  • 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.
  • 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.
  • This series of courses teaches participants the following skills:


Viewing outline for:
Recognize the AI/ML framework 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.


Some familiarity with basic machine learning concepts
  • Basic proficiency with a scripting language, preferably Python
  • To get the most out of this specialization, participants should have:

    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
    • This class is intended for the following participants:

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