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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 big data pipeline and machine learning models with Vertex AI on Google Cloud.

What you Will learn

What's Included?

Instructor Live Training

An instructor will answer your questions

OFFICIAL GOOGLE CLOUD CONTENT

Course content reflects the latest google cloud class

hands on labs

Real world hands on labs provided by Qwiklabs and supported by instructor

CertIficate of completion

Receive official certificate on completion of 80% of labs

Who's this course for?

LeveL

Language

duration

Prerequisites

Products

Course Content

Topics

  • 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 1: Exploring a BigQuery Public Dataset

Topics

  • 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.
  • Build collaborative real-time dashboards with data visualization tools.

Activities

  • Lab 2: Creating a Streaming Data Pipeline for a Real-Time Dashboard with Dataflow

Topics

  • 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.

Objectives

  • 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.

Activities

  • Lab 3: Predicting Visitor Purchases using BigQuery ML

Topics

  • 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

Topics

  • 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.

Activities

  • Lab 4: Vertex AI: Predicting Loan Risk with AutoML

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Upcoming Schedule

T-GCPBDML-B-03