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Course Outline

Fundamentals of AI Deployment

  • Insight into the AI deployment lifecycle
  • Obstacles encountered when moving AI agents to production
  • Critical factors: scalability, reliability, and long-term maintainability

Containerization and Orchestration Strategies

  • Basics of Docker and containerization
  • Employing Kubernetes to orchestrate AI agents
  • Best practices for overseeing containerized AI applications

Serving AI Models

  • Review of model serving frameworks (such as TensorFlow Serving, TorchServe)
  • Creating REST APIs for AI agent inference
  • Managing the distinction between batch and real-time predictions

CI/CD for AI Agents

  • Configuring CI/CD pipelines specific to AI deployments
  • Automating the testing and validation of AI models
  • Executing rolling updates and managing version control

Monitoring and Optimization

  • Implementing monitoring solutions for AI agent performance
  • Assessing model drift and identifying retraining requirements
  • Refining resource usage and scalability

Security and Governance

  • Complying with data privacy regulations
  • Protecting AI deployment pipelines and APIs
  • Conducting audits and maintaining logs for AI applications

Practical Exercises

  • Containerizing an AI agent using Docker
  • Deploying an AI agent via Kubernetes
  • Configuring monitoring for AI performance and resource consumption

Conclusion and Future Directions

Requirements

  • Strong command of Python programming
  • A solid grasp of machine learning workflows
  • Working knowledge of containerization platforms like Docker
  • Background in DevOps practices (advisable)

Target Audience

  • MLOps engineers
  • DevOps specialists
 14 Hours

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