Deploying AI Agents in Production Environments Training Course

Introduction to AI Deployment

• An overview of the AI deployment lifecycle.
• Key challenges associated with moving AI agents to production.
• Critical factors to consider: scalability, reliability, and maintainability.

Containerization and Orchestration

• Foundations of Docker and containerization concepts.
• Utilizing Kubernetes for orchestrating AI agents.
• Best practices for managing containerized AI applications.

Serving AI Models

• Introduction to model serving frameworks such as TensorFlow Serving and TorchServe.
• Developing REST APIs for AI agent inference.
• Managing batch processing versus real-time predictions.

CI/CD for AI Agents

• Configuring CI/CD pipelines specifically for AI deployments.
• Automating the testing and validation of AI models.
• Implementing rolling updates and managing version control.

Monitoring and Optimization

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

Security and Governance

• Ensuring compliance with data privacy regulations.
• Securing AI deployment pipelines and associated APIs.
• Implementing auditing and logging for AI applications.

Hands-On Activities

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

Summary and Next Steps