Google Cloud Fundamentals: Big Data and Machine Learning, Virtual BST

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.

Course Objectives

• Recognize 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.

Audience

• 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

Prerequisites

Basic understanding of one or more of the following:

• Database query language such as SQL
• Data engineering workflow from extract, transform, load, to analysis, modeling, and deployment
• Machine learning models such as supervised versus unsupervised models

Course Outline

The course includes presentations, demonstrations, and hands-on labs.

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

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

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

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

Module 6: Summary

• Recap of key learning points
• Resources

**This event is managed by Google and ROI Training. By registering you may receive communications from either organization