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Course Outline

Introduction to Vertex AI and Machine Learning Platforms

  • Overview of artificial intelligence and machine learning workflows.
  • Introduction to Google Cloud Vertex AI.
  • Understanding the architecture and key components of Vertex AI.
  • Exploring the role of Vertex AI in developing and deploying machine learning solutions.

Setting Up the Vertex AI Environment

  • Configuring a Google Cloud project for Vertex AI use.
  • Understanding workspaces, resources, and necessary permissions.
  • Preparing datasets and development environments.
  • Navigating through Vertex AI tools and interfaces.

Machine Learning Fundamentals with Vertex AI

  • Understanding core supervised learning concepts.
  • Overview of regression and classification models.
  • Preparing data for machine learning workflows.
  • Evaluating model performance and accuracy.

Natural Language Processing (NLP) with Vertex AI

  • Introduction to NLP concepts.
  • Understanding text-based machine learning applications.
  • Preparing and processing text data.
  • Exploring NLP capabilities within the Vertex AI ecosystem.

Building and Training Machine Learning Models

  • Preparing training code for Vertex AI.
  • Containerizing machine learning training applications.
  • Configuring training jobs.
  • Running and monitoring model training processes.

Deploying Machine Learning Models

  • Understanding the workflow for deploying models.
  • Creating model endpoints.
  • Deploying trained models to generate predictions.
  • Managing deployed models and associated resources.

Monitoring and Troubleshooting Vertex AI Solutions

  • Monitoring training and deployment activities.
  • Identifying common configuration issues.
  • Troubleshooting problems related to model execution.
  • Applying best practices for reliable ML workflows.

Practical Workshop and Course Review

  • Constructing a complete machine learning workflow using Vertex AI.
  • Training and deploying a sample model.
  • Reviewing key Vertex AI features and capabilities.
  • Discussing next steps for advanced machine learning development.

Requirements

  • Basic knowledge of machine learning principles.

Audience

  • Software engineers.
  • Machine learning enthusiasts.
 7 Hours

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