Course Outline

Introduction to AI Deployment

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

Containerization and Orchestration

  • Introduction to Docker and containerization basics
  • Using Kubernetes for AI agent orchestration
  • Best practices for managing containerized AI applications

Serving AI Models

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

CI/CD for AI Agents

  • Setting up CI/CD pipelines for AI deployments
  • Automating testing and validation of AI models
  • Rolling updates and managing version control

Monitoring and Optimization

  • Implementing monitoring tools for AI agent performance
  • Analyzing model drift and retraining needs
  • Optimizing resource utilization and scalability

Security and Governance

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

Hands-On Activities

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

Summary and Next Steps

Requirements

  • Proficiency in Python programming
  • Understanding of machine learning workflows
  • Familiarity with containerization tools like Docker
  • Experience with DevOps practices (recommended)

Audience

  • MLOps engineers
  • DevOps professionals
 14 Hours

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