Google Cloud Big Data and Machine Learning Fundamentals (edX)

This course is part of the Google Cloud Data Engineer Learning Path Professional Certificate.

What you'll learn

• Identify the data-to-AI lifecycle on Google Cloud and the major products of big data and machine learning.
• Design streaming pipelines with Dataflow and Pub/Sub.
• Analyze big data at scale with BigQuery.
• Identify different options to build machine learning solutions on Google Cloud.
• Describe a machine learning workflow and the key steps with Vertex AI.
• Build a machine learning pipeline using AutoML.

Syllabus

1. Course Introduction

This section welcomes learners to the Big Data and Machine Learning Fundamentals course, and provides an overview of the course structure and goals.

1. Big Data and Machine Learning on Google Cloud

This section explores the key components of Google Cloud's infrastructure. It's here that we introduce many of the big data and machine learning products and services that support the data-to AI lifecycle on Google Cloud.

1. Data Engineering for Streaming Data

This section introduces Google Cloud's solution to managing streaming data. It examines an end-to-end pipeline, including data ingestion with Pub/Sub, data processing with Dataflow, and data visualization with Looker and Data Studio.

1. Big Data with BigQuery

This section introduces learners to BigQuery, Google's fully-managed, serverless data warehouse. It also explores BigQuery ML, and the processes and key commands that are used to build custom machine learning models.

1. Machine Learning Options on Google Cloud

This section explores four different options to build machine learning models on Google Cloud. It also introduces Vertex AI, Google's unified platform for building and managing the lifecycle of ML projects.

1. The Machine Learning Workflow with Vertex AI

This section focuses on the three key phases--data preparation, model training, and model preparation--of the machine learning workflow in Vertex AI. Learners get the opportunity to practice building a machine learning model with AutoML.

1. Course Summary

This section reviews the topics covered in the course, and provides additional resources for further learning.