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Course Outline

Foundations of AI Deployment

  • Insight into the AI deployment lifecycle
  • Navigating the challenges of transitioning AI agents to production
  • Critical factors: scalability, reliability, and maintainability

Containerization and Orchestration Strategies

  • Basics of Docker and containerization techniques
  • Leveraging Kubernetes for the orchestration of AI agents
  • Best practices for overseeing containerized AI applications

AI Model Serving

  • Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
  • Developing REST APIs for AI agent inference
  • Managing batch processing versus real-time predictions

CI/CD Pipelines for AI Agents

  • Establishing CI/CD pipelines specifically for AI deployments
  • Automation of testing and validation processes for AI models
  • Implementing rolling updates and managing version control

Monitoring and Performance Optimization

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

Security and Governance Protocols

  • Ensuring adherence to data privacy regulations
  • Hardening AI deployment pipelines and APIs against threats
  • Implementing auditing and logging mechanisms 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

  • Advanced proficiency in Python programming
  • A solid grasp of machine learning workflows
  • Familiarity with containerization tools, particularly Docker
  • Practical experience with DevOps practices (recommended)

Target Audience

  • MLOps Engineers
  • DevOps Professionals
 14 Hours

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