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Course Outline

Foundations of AI Deployment

  • The AI deployment lifecycle at a glance
  • Obstacles encountered when moving AI agents to production
  • Critical factors: scalability, dependability, and ease of maintenance

Containerization & Orchestration

  • Basics of Docker and container technology
  • Leveraging Kubernetes for orchestrating AI agents
  • Best practices for overseeing containerized AI applications

AI Model Serving

  • Introduction to model serving frameworks (e.g., TensorFlow Serving, TorchServe)
  • Constructing REST APIs for AI agent inference
  • Managing batch versus real-time prediction tasks

CI/CD for AI Agents

  • Configuring CI/CD pipelines for AI release cycles
  • Streamlining the testing and validation of AI models
  • Executing rolling updates and overseeing version control

Performance Monitoring & Optimization

  • Deploying monitoring solutions to track AI agent performance
  • Evaluating model drift and identifying retraining requirements
  • Enhancing resource efficiency and scalability

Security & Governance

  • Meeting compliance standards for data privacy
  • Protecting AI deployment pipelines and API endpoints
  • Implementing audit trails and logging for AI systems

Practical Labs

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

Recap & Future Directions

Requirements

  • Strong command of Python programming
  • Comprehension of machine learning workflows
  • Knowledge of containerization tools such as Docker
  • Exposure to DevOps methodologies (suggested)

Target Audience

  • MLOps engineers
  • DevOps specialists
 14 Hours

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