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Course Outline

Foundations of AI Deployment

  • Overview of the end-to-end AI deployment lifecycle.
  • Addressing the specific challenges of moving AI agents to production.
  • Key factors to consider: scalability, reliability, and maintainability.

Containerization and Orchestration Strategies

  • Basics of Docker and the principles of containerization.
  • Leveraging Kubernetes for the orchestration of AI agents.
  • Best practices for the management of containerized AI applications.

Serving AI Models

  • Introduction to model serving frameworks (e.g., TensorFlow Serving, TorchServe).
  • Developing REST APIs for AI agent inference.
  • Distinguishing between and managing batch versus real-time predictions.

CI/CD Pipelines for AI Agents

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

Monitoring and Performance Optimization

  • Implementing monitoring tools to track AI agent performance.
  • Analyzing model drift and identifying retraining necessities.
  • Optimizing resource usage and ensuring scalability.

Security and Governance Frameworks

  • Maintaining compliance with data privacy regulations.
  • Securing AI deployment pipelines and associated APIs.
  • Auditing and logging practices for AI applications.

Practical Application

  • Containerizing an AI agent using Docker.
  • Deploying an AI agent through Kubernetes.
  • Establishing monitoring for AI performance and resource consumption.

Conclusion and Future Directions

Requirements

  • Strong proficiency in Python programming.
  • A solid grasp of machine learning workflows.
  • Working knowledge of containerization technologies, specifically Docker.
  • Background in DevOps practices (advised).

Target Audience

  • MLOps engineers.
  • DevOps specialists.
 14 Hours

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