Generative AI on GCP: Empowering Machine Language Workflows with Vertex AI

Google Cloud    |    Intermediate
  • 12 videos | 1h 39m 12s
  • Includes Assessment
  • Earns a Badge
Google Cloud Platform (GCP) has a broad range of powerful generative AI tools that can be used to leverage the power of modern artificial intelligence (AI). A main component of that toolbox is Google's Vertex AI platform, which can aid organizations in streamlining machine learning (ML) workflows and deliver better machine learning models. In this course, you'll be introduced to Vertex AI, beginning with an overview of its features, model deployment, and end-to-end workflow. Then you'll explore pros and cons of Vertex AI, how it can be used to accelerate development, and model selection, training, and evaluation considerations. Finally, you'll learn about Vertex AI integration, data preparation, feature engineering, model evaluation, and Vertex AI success stories.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Outline the fundamentals of vertex ai and its role in the machine learning (ml) life cycle
    Identify key features of vertex ai, including mlops, automl, custom training, and interfaces
    Deploy a basic model using vertex ai
    Automate the end-to-end ml workflow using vertex ai
    Identify advantages and benefits of using vertex ai for developing and deploying ml models
  • Outline how vertex ai accelerates model development and deployment processes
    Select, train, tune, and evaluate models using vertex ai
    Integrate vertex ai with popular artificial intelligence frameworks
    Identify best practices for data preparation, feature engineering, and model evaluation with vertex ai
    Outline real-world use cases and success stories of organizations using vertex ai
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 55s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 6m 32s
    After completing this video, you will be able to outline the fundamentals of Vertex AI and its role in the machine learning (ML) life cycle. FREE ACCESS
  • Locked
    3.  Vertex AI Features
    12m 18s
    Upon completion of this video, you will be able to identify key features of Vertex AI, including MLOps, AutoML, custom training, and interfaces. FREE ACCESS
  • Locked
    4.  Deploying a Model with Vertex AI
    10m 22s
    Learn how to deploy a basic model using Vertex AI. FREE ACCESS
  • Locked
    5.  Simplifying End-to-end Workflow with Vertex AI
    11m 22s
    In this video, find out how to automate the end-to-end ML workflow using Vertex AI. FREE ACCESS
  • Locked
    6.  Pros and Cons of Vertex AI
    6m 56s
    After completing this video, you will be able to identify advantages and benefits of using Vertex AI for developing and deploying ML models. FREE ACCESS
  • Locked
    7.  How Vertex AI Accelerates Development and Deployment
    11m 55s
    Upon completion of this video, you will be able to outline how Vertex AI accelerates model development and deployment processes. FREE ACCESS
  • Locked
    8.  Selecting, Training, and Evaluating Models
    10m 55s
    Discover how to select, train, tune, and evaluate models using Vertex AI. FREE ACCESS
  • Locked
    9.  Integrating Vertex AI with AI Frameworks
    8m 19s
    In this video, you will learn how to integrate Vertex AI with popular artificial intelligence frameworks. FREE ACCESS
  • Locked
    10.  Data Preparation, Feature Engineering, and Model Evaluation
    11m 26s
    After completing this video, you will be able to identify best practices for data preparation, feature engineering, and model evaluation with Vertex AI. FREE ACCESS
  • Locked
    11.  Vertex AI Success Stories
    7m 21s
    Upon completion of this video, you will be able to outline real-world use cases and success stories of organizations using Vertex AI. FREE ACCESS
  • Locked
    12.  Course Summary
    53s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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