Module 1: Big Data and Machine Learning on Google Cloud

• Identify the different aspects of Google Cloud’s infrastructure.
• Identify the big data and machine learning products on Google Cloud.
• Lab: Exploring a BigQuery Public Dataset

Module 2: Data Engineering for Streaming Data

• Describe an end-to-end streaming data workflow from ingestion to data visualization.
• Identify modern data pipeline challenges and how to solve them at scale with Dataflow.
• Build collaborative real-time dashboards with data visualization tools.
• Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow

Module 3: Big Data with BigQuery

• Describe the essentials of BigQuery as a data warehouse.
• Explain how BigQuery processes queries and stores data.
• Define BigQuery ML project phases.
• Build a custom machine learning model with BigQuery ML.
• Lab: Predicting Visitor Purchases Using BigQuery ML

Module 4: Machine Learning Options on Google Cloud

• Identify different options to build ML models on Google Cloud.
• Define Vertex AI and its major features and benefits.
• Describe AI solutions in both horizontal and vertical markets.

Module 5: The Machine Learning Workflow with Vertex AI

• Describe a ML workflow and the key steps.
• Identify the tools and products to support each stage.
• Build an end-to-end ML workflow using AutoML.
• Lab: Vertex AI: Predicting Loan Risk with AutoML

Module 5: Course Summary

This section reviews the topics covered in the course and provides additional resources for further learning.

Describe the data-to-AI lifecycle on Google Cloud and identify the major products of big data and machine learning.

Basic understanding of one or more of the following:

• Database query language such as SQL
• Data engineering workflow from extract, transform, load, to analysis, modeling, and deployment
• Machine learning models such as supervised versus unsupervised models

This course teaches participants the following skills:

• Recognize the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
• Design streaming pipelines with Dataflow and Pub/Sub.
• Analyze big data at scale with BigQuery.
• Identify different options to build machine learning solutions on Google Cloud.
• Describe a machine learning workflow and the key steps with Vertex AI.
• Build a machine learning pipeline using AutoML.

• Module 1: Big Data and Machine Learning on Google Cloud
• Module 2: Data Engineering for Streaming Data
• Module 3: Big Data with BigQuery
• Module 4: Machine Learning Options on Google Cloud
• Module 5: The Machine Learning Workflow with Vertex AI
• Module 6: Course Summary