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Course Outline

Introduction to Vertex AI and Machine Learning Platforms

  • Fundamentals of artificial intelligence and machine learning workflows
  • Getting acquainted with Google Cloud Vertex AI
  • Understanding the architecture and core components of Vertex AI
  • Examining the role of Vertex AI in developing and deploying machine learning solutions

Setting Up the Vertex AI Environment

  • Configuring your Google Cloud project for Vertex AI
  • Understanding workspaces, resources, and permission management
  • Preparing datasets and development environments
  • Navigating the tools and interfaces within Vertex AI

Machine Learning Fundamentals with Vertex AI

  • Core concepts of supervised learning
  • Overview of regression and classification models
  • Data preparation strategies for machine learning workflows
  • Assessing model performance and accuracy

Natural Language Processing (NLP) with Vertex AI

  • Introduction to NLP principles
  • Text-based machine learning applications
  • Techniques for preparing and processing text data
  • Exploring NLP capabilities within the Vertex AI platform

Building and Training Machine Learning Models

  • Writing training code compatible with Vertex AI
  • Containerizing machine learning training applications
  • Configuring training jobs
  • Executing and monitoring model training processes

Deploying Machine Learning Models

  • Understanding the workflow for model deployment
  • Creating model endpoints
  • Deploying trained models for predictive analysis
  • Managing deployed models and associated resources

Monitoring and Troubleshooting Vertex AI Solutions

  • Tracking training and deployment activities
  • Identifying common configuration issues
  • Resolving model execution problems
  • Implementing 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 features and capabilities of Vertex AI
  • Discussing pathways for advanced machine learning development

Requirements

  • Familiarity with machine learning

Audience

  • Software engineers
  • Machine learning enthusiasts
 7 Hours

Testimonials (4)

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