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Smart Analytics, Machine Learning, and AI on Google Cloud

Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For...

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$900 USD
Course Code GCP-SAML-AI
Duration 1 day
Available Formats Classroom, Virtual

Incorporating machine learning into data pipelines increases the ability to extract insights from data. This course covers ways machine learning can be included in data pipelines on Google Cloud. For little to no customization, this course covers AutoML. For more tailored machine learning capabilities, this course introduces Notebooks and BigQuery machine learning (BigQuery ML). Also, this course covers how to productionalize machine learning solutions by using Vertex AI.

Skills Gained

  • Differentiate between ML, AI and deep learning.
  • Discuss the use of ML API’s on unstructured data.
  • Execute BigQuery commands from notebooks.
  • Create ML models by using SQL syntax in BigQuery.
  • Create ML models without coding using Vertex AI AutoML.

Who Can Benefit

  • Data Engineers

Prerequisites

To benefit from this course, participants should have completed “Google Cloud Big Data and Machine Learning Fundamentals” or have equivalent experience.

Course Details

Course Thumbnail

Introduction

  • In this module, we introduce the course and agenda

Introduction to Analytics and AI

  • This modules talks about ML options on Google Cloud

Prebuilt ML Model APIs for Unstructured Data

  • This module focuses on using pre-built ML APIs on your unstructured data

Big Data Analytics with Notebooks

  • This module covers how to use Notebooks

Production ML Pipelines

  • This module covers building custom ML models and introduces Vertex AI and TensorFlow Hub

Custom Model Building with SQL in BigQuery ML

  • This module covers BigQuery ML

Custom Model Building with Vertex AI AutoML

  • Custom model building with Vertex AI AutoML

Summary

  • This module recaps the topics covered in the course

Course Resources

  • PDF links to all modules
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