Google Cloud Big Data and Machine Learning Fundamentals

Module 1) Course Introduction

• Recognize the data-to-AI lifecycle on Google Cloud
• Identify the connection between data engineering and machine learning.

Module 2) Big Data and Machine Learning on Google Cloud

• Identify how elements of the Google Cloud infrastructure have enabled big data and machine learning capabilities.
• Identify the big data and machine learning products on Google Cloud.
• Explore a BigQuery dataset.

Module 3) 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.
• Create a streaming data pipeline for a real-time dashboard with Dataflow.

Module 4) Big Data with BigQuery

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

Module 5) 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 6) The Machine Learning Workflow with Vertex AI

• Describe the ML workflow and the key steps.
• Identify the tools and products to support each stage.
• Build an end-to-end ML workflow using AutoML.

Module 7) Course Summary

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