Introducing Google Cloud Platform

• Identify the different aspects of Google Cloud’s infrastructure.

• Identify the big data and ML products that form Google Cloud.

Recommending Products Using Cloud SQL and Spark

• Review how businesses use recommendation models.

• Evaluate how and where you will compute and store your housing rental model results.

• Analyze how running Hadoop in the cloud with Dataproc can enable scale.

• Evaluate different approaches for storing recommendation data off-cluster.

Predicting Visitor Purchases Using BigQuery ML

• Analyze big data at scale with BigQuery.

• Learn how BigQuery processes queries and stores data at scale.

• Walkthrough key ML terms: features, labels, training data.

• Evaluate the different types of models for structured datasets.

• Create custom ML models with BigQuery ML.

Real-time Dashboards with Pub/Sub, Dataflow, and Google Data Studio

• Identify modern data pipeline challenges and how to solve them at scale with Dataflow.

• Design streaming pipelines with Apache Beam.

• Build collaborative real-time dashboards with Data Studio.

Deriving Insights from Unstructured Data Using Machine Learning

• Evaluate how businesses use unstructured ML models and how the models work.

• Choose the right approach for machine learning models between pre-built and custom.

• Create a high-performing custom image classification model with no code using AutoML.

Summary

• Recap of key learning points.

• Resources.