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Google Cloud Big Data and Machine Learning Fundamentals

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...

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$675 USD GSA  $504.01
Course Code GCP-BD-ML
Duration 1 day
Available Formats Classroom, Virtual
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“Training was really good and loved having examples to test against the cloud. The training format was also really nice with live interaction but comfort of being at home for the training.”

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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.

Skills Gained

This course teaches participants the following skills:

  • 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.

Who Can Benefit

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

Prerequisites

To get the most of out of this course, participants should have:

  • 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 Details

Course Outline

Module 0: Course Introduction

  • Recognize the data-to-AI lifecycle on Google Cloud
  • Identify the connection between data engineering and machine learning

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.
  • Lab: Exploring a BigQuery Public Dataset
  • Quiz

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.
  • Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
  • Quiz

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.
  • Lab: Predicting Visitor Purchases Using BigQuery ML
  • Quiz

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.
  • Quiz

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.
  • Lab: Vertex AI: Predicting Loan Risk with AutoML
  • Quiz

Module 6: Course Summary

  • This section reviews the topics covered in the course and provides additional resources for further learning.
  • Describe the data-to-AI lifecycle on Google Cloud and identify the major products of big data and machine learning.
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