Google Cloud Fundamentals: Big Data and Machine Learning (GCF-BDM) – Outline

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.