Detailed Course Outline
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.