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Course Outline

Introduction to AI Deployment

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

Containerization and Orchestration

  • Introduction to Docker and containerization fundamentals.
  • Utilizing 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 versus real-time predictions.

CI/CD for AI Agents

  • Setting up CI/CD pipelines for AI deployments.
  • Automating testing and validation of AI models.
  • Managing rolling updates and version control.

Monitoring and Optimization

  • Implementing monitoring tools for 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.
  • 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.
  • 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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