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

Exploring a BigQuery Public Datasets

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

Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow

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

Predicting Visitor Purchases with a Classification Model with BigQuery ML

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

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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

Vertex AI: Predicting Loan Risk with AutoML