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Course Outline

Introduction to AI Deployment

  • Overview of the AI deployment lifecycle
  • Challenges associated with deploying AI agents to production
  • Key considerations: scalability, reliability, and maintainability

Containerization and Orchestration

  • Foundations of Docker and containerization
  • Utilizing Kubernetes for orchestrating AI agents
  • Best practices for managing containerized AI applications

Serving AI Models

  • Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
  • Developing REST APIs for AI agent inference
  • Managing batch versus real-time predictions

CI/CD for AI Agents

  • Configuring CI/CD pipelines for AI deployments
  • Automating the testing and validation of AI models
  • Managing rolling updates and version control

Monitoring and Optimization

  • Implementing tools to monitor AI agent performance
  • Analyzing model drift and identifying retraining needs
  • Optimizing resource utilization and scalability

Security and Governance

  • Ensuring compliance with data privacy regulations
  • Securing AI deployment pipelines and APIs
  • Implementing auditing and logging for AI applications

Hands-On Activities

  • Containerizing an AI agent using Docker
  • Deploying an AI agent via Kubernetes
  • Setting up monitoring for AI performance and resource usage

Summary and Next Steps

Requirements

  • Proficiency in Python programming
  • A solid understanding of machine learning workflows
  • Familiarity with containerization tools, particularly Docker
  • Experience with DevOps practices (recommended)

Target Audience

  • MLOps engineers
  • DevOps professionals
 14 Hours

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