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Course Outline
Foundations of AI Deployment
- Review of the AI deployment lifecycle
- Key challenges when releasing AI agents to production
- Core priorities: scalability, reliability, and maintainability
Containerization and Orchestration Strategies
- Basics of Docker and containerization
- Orchestrating AI agents with Kubernetes
- Best practices for overseeing containerized AI applications
Model Serving Mechanisms
- Introduction to model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Creating REST APIs for AI agent inference
- Distinguishing between batch and real-time prediction handling
CI/CD Integration for AI Agents
- Configuring CI/CD pipelines for AI release processes
- Automating the testing and validation of AI models
- Managing rolling updates and version control
Performance Monitoring and Tuning
- Deploying monitoring tools to track AI agent performance
- Evaluating model drift and retraining requirements
- Enhancing resource efficiency and scalability
Security and Governance Protocols
- Maintaining compliance with data privacy laws
- Protecting AI deployment pipelines and APIs
- Implementing auditing and logging for AI applications
Practical Exercises
- Containerizing an AI agent using Docker
- Releasing an AI agent via Kubernetes
- Configuring monitoring for AI performance and resource consumption
Recap and Future Directions
Requirements
- Strong command of Python programming
- Solid grasp of machine learning workflows
- Working knowledge of containerization platforms such as Docker
- Exposure to DevOps methodologies (advisable)
Target Audience
- MLOps engineers
- DevOps specialists
14 Hours
