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Course Outline

Foundations of Artificial Intelligence Deployment for Government

  • Comprehensive overview of the end-to-end AI deployment lifecycle
  • Addressing operational challenges associated with deploying AI agents in production environments
  • Strategic considerations for scalability, system reliability, and long-term maintainability

Containerization and Orchestration Standards

  • Fundamentals of Docker and container-based application architecture
  • Leveraging Kubernetes for the orchestration and management of AI agents
  • Industry best practices for governing containerized AI workloads

AI Model Serving Architectures

  • Evaluation of model serving frameworks, including TensorFlow Serving and TorchServe
  • Development of RESTful APIs to facilitate AI agent inference operations
  • Strategies for managing both batch processing and real-time prediction workflows

Continuous Integration and Continuous Deployment (CI/CD) for AI

  • Establishing robust CI/CD pipelines tailored for AI deployment cycles
  • Automation of testing protocols and model validation processes
  • Implementation of rolling updates and rigorous version control management

Performance Monitoring and System Optimization

  • Deployment of monitoring solutions to track AI agent performance metrics
  • Analysis of model drift and determination of retraining requirements
  • Optimization strategies for resource efficiency and horizontal scalability

Security, Governance, and Compliance

  • Ensuring adherence to data privacy regulations and statutory requirements
  • Hardening AI deployment pipelines and securing associated API endpoints
  • Implementation of comprehensive auditing and logging mechanisms for AI applications

Practical Implementation Exercises

  • Step-by-step containerization of an AI agent utilizing Docker
  • Execution of AI agent deployment via Kubernetes infrastructure
  • Configuration of monitoring tools to assess performance and resource consumption

Conclusion and Future Operational Steps

Requirements

  • Proficiency in Python programming
  • Foundational understanding of machine learning workflows
  • Familiarity with containerization technologies, including Docker
  • Professional experience with DevOps practices (recommended)

Target Audience

  • MLOps Engineers
  • DevOps Professionals
 14 Hours

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