Google Cloud Big Data and Machine Learning Fundamentals

1 days
GCPBigData - Version:1
Description
This course introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud
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  • 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.
    • Objectives:
    • • Recognize the data-to-AI lifecycle on Google Cloud
    • • Identify the connection between data engineering and machine learning

  • Big Data and Machine Learning on Google Cloud
    • This section explores the key components of Google Cloud's infrastructure. We
    • introduce many of the big data and machine learning products and services that
    • support the data-to AI lifecycle on Google Cloud.
    • Objectives:
    • • Identify the different aspects of Google Cloud’s infrastructure.
    • • Identify the big data and machine learning products on Google Cloud.
    • Activities:
    • • Lab: Exploring a BigQuery Public Dataset
    • • Quiz

  • 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 Data Studio.
    • Objectives:
    • • 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.
    • Activities:
    • • Build collaborative real-time dashboards with data visualization tools.
    • • Lab: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow
    • • Quiz

  • 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.
    • • Describe the essentials of BigQuery as a data warehouse.
    • • Explain how BigQuery processes queries and stores data.
    • Objectives:
    • • Define BigQuery ML project phases.
    • • Build a custom machine learning model with BigQuery ML.
    • Activities:
    • • Lab: Predicting Visitor Purchases Using BigQuery ML
    • • Quiz

  • 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.
    • Objectives:
    • • 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.
    • Activities- Quiz

  • 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 can
    • practice building a machine learning model with AutoML.
    • Objectives:
    • • 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

  • Course Summary
    • This section reviews the topics covered in the course and provides additional
    • resources for further learning.
    • Objectives:
    • Describe the data-to-AI lifecycle on Google Cloud and identify the major products of
    • big data and machine learning.
  • Recognize the data-to-AI lifecycle on Google Cloud and the major big data and machine learning products.
  • 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
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03-6176677 info@sela.co.il

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