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Course Outline

Foundations of AI Deployment

  • Understanding the complete AI deployment lifecycle
  • Navigating challenges associated with releasing AI agents to production
  • Core focus areas: scalability, reliability, and maintainability

Containerization and Orchestration Strategies

  • Basics of Docker and the fundamentals of containerization
  • Utilizing Kubernetes for orchestrating AI agents
  • Best practices for maintaining containerized AI applications

AI Model Serving Mechanisms

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

CI/CD Integration for AI Agents

  • Configuring CI/CD pipelines tailored for AI deployments
  • Automating the testing and validation processes for AI models
  • Implementing rolling updates and version control management

Performance Monitoring and Optimization

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

Security Frameworks and Governance

  • Meeting data privacy regulatory compliance standards
  • Protecting AI deployment pipelines and API endpoints
  • Implementing auditing and logging protocols for AI applications

Practical Laboratory Sessions

  • Containerizing an AI agent using Docker
  • Deploying an AI agent within a Kubernetes cluster
  • Establishing monitoring for AI performance and resource consumption

Conclusion and Future Directions

Requirements

  • Strong proficiency in Python programming
  • Comprehensive understanding of machine learning workflows
  • Working knowledge of containerization tools such as Docker
  • Practical experience with DevOps practices (suggested)

Target Audience

  • MLOps engineers
  • DevOps professionals
 14 Hours

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