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.
