Duration: 1 day 

This course introduces the Google Cloud big data and machine learning products and services that support the data-to-AI lifecycle. It explores the processes, challenges, and benefits of building a big data pipeline and machine learning models with Vertex AI on Google Cloud.

Audience

This class is intended for the following: 

• Data analysts, data scientists, and business analysts who are getting started with Google Cloud.

• Individuals responsible for designing pipelines and architectures for data processing, creating and maintaining machine learning and statistical models, querying datasets, visualizing query results, and creating reports.

• Executives and IT decision makers evaluating Google Cloud for use by data scientists.

Objectives

• Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.

• Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.

• Employ BigQuery and Cloud SQL to carry out interactive data analysis.

• Choose between different data processing products in Google Cloud.

• Create ML models with BigQuery ML, ML APIs, and AutoML.

Brief Syllabus

1. Introducing Google Cloud Platform

2. Compute and Storage Fundamentals

3. Data Analytics on the Cloud

4. Scaling Data Analysis

5. Machine Learning

6. Data Processing Architectures

7. Summary