Module 1: Course Introduction

• Understand the data-to-AI lifecycle on Google Cloud.
• Explore the relationship between data engineering and machine learning.

Module 2: Big Data and Machine Learning on Google Cloud

• Learn how Google Cloud infrastructure supports big data and machine learning.
• Identify core Google Cloud products for big data and ML.
• Explore datasets in BigQuery to understand data analysis capabilities.

Module 3: Data Engineering for Streaming Data

• Describe the end-to-end workflow for streaming data, from ingestion to visualization.
• Solve large-scale data pipeline challenges using Dataflow.
• Build collaborative, real-time dashboards with visualization tools.
• Create a streaming data pipeline for real-time insights.

Module 4: Big Data with BigQuery

• Explore BigQuery as a powerful data warehouse solution.
• Understand how BigQuery processes and stores data.
• Dive into the phases of a BigQuery ML project.
• Build a custom machine learning model directly within BigQuery.

Module 5: Machine Learning Options on Google Cloud

• Compare options for creating machine learning models on Google Cloud.
• Understand the features and benefits of Vertex AI.
• Explore use cases for AI solutions in horizontal and vertical markets.

Module 6: The Machine Learning Workflow with Vertex AI

• Understand the end-to-end machine learning workflow.
• Learn the tools and products for each stage of the ML lifecycle.
• Build a complete ML pipeline using AutoML.

Module 7: Course Summary

• Review the data-to-AI lifecycle on Google Cloud.
• Summarize the major products and services for big data and machine learning.

This course provides foundational knowledge and practical skills to leverage Google Cloud for big data analysis and machine learning development, equipping participants to build scalable, data-driven solutions.