Google Cloud Big Data and Machine Learning Fundamentals

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Overview

Google Cloud Big Data and Machine Learning Fundamentals is an introductory course designed to familiarize learners with the core concepts and services related to big data and machine learning on the Google Cloud Platform (GCP). Ideal for data engineers, analysts, and developers, this course provides a comprehensive foundation to build scalable, data-driven solutions using Google Cloud. The course covers key big data technologies and tools such as BigQuery, Cloud Storage, and Dataflow, highlighting how they integrate within the GCP ecosystem to...

Syllabus

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.

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.

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 Looker Studio.

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.

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.

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.