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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.

Who is this training for?

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

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

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

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

Machine Learning on Google Cloud Training Course in South Africa

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Expand your IT knowledge with our comprehensive range of courses, including programming, software development, business skills, data science, cybersecurity, cloud computing and virtualization. Our skilled instructors will facilitate hands-on training and share practical insights, all conveniently conducted at your preferred location within South Africa.

Machine Learning on Google Cloud Training in South Africa

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Machine Learning on Google Cloud Training in South Africa

Prerequisites

What You Will Learn

Outline

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