Course Outline

Introduction to AI Deployment for Government

  • Overview of the AI deployment lifecycle for government applications
  • Challenges in deploying AI agents to production environments within the public sector
  • Key considerations: scalability, reliability, and maintainability for government systems

Containerization and Orchestration for Government

  • Introduction to Docker and containerization basics for government use cases
  • Using Kubernetes for AI agent orchestration in government operations
  • Best practices for managing containerized AI applications in the public sector

Serving AI Models for Government

  • Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe) for government applications
  • Building REST APIs for AI agent inference in government systems
  • Handling batch versus real-time predictions for government operations

CI/CD for AI Agents in Government

  • Setting up CI/CD pipelines for AI deployments in government agencies
  • Automating testing and validation of AI models for government use
  • Rolling updates and managing version control for government systems

Monitoring and Optimization for Government

  • Implementing monitoring tools for AI agent performance in government operations
  • Analyzing model drift and retraining needs for government applications
  • Optimizing resource utilization and scalability for government systems

Security and Governance for Government

  • Ensuring compliance with data privacy regulations in government operations
  • Securing AI deployment pipelines and APIs for government use
  • Auditing and logging for AI applications in the public sector

Hands-On Activities for Government

  • Containerizing an AI agent with Docker for government systems
  • Deploying an AI agent using Kubernetes for government operations
  • Setting up monitoring for AI performance and resource usage in government applications

Summary and Next Steps for Government

Requirements

  • Proficiency in Python programming for government applications
  • Understanding of machine learning workflows and their integration into public sector projects
  • Familiarity with containerization tools, such as Docker, to enhance deployment efficiency
  • Experience with DevOps practices (recommended) to support continuous integration and delivery processes for government systems

Audience

  • MLOps engineers working in the public sector
  • DevOps professionals supporting government initiatives
 14 Hours

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