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Course Outline
Foundations of AI Deployment
- Comprehensive overview of the AI deployment lifecycle
- Key challenges associated with releasing AI agents to production
- Core priorities: ensuring scalability, reliability, and maintainability
Containerization and Orchestration
- Foundational concepts of Docker and containerization
- Utilizing Kubernetes for the orchestration of AI agents
- Best practices for managing containerized AI workloads
AI Model Serving
- Overview of leading model serving frameworks, including TensorFlow Serving and TorchServe
- Developing REST APIs for AI agent inference tasks
- Strategies for handling 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
- Managing rolling updates and version control strategies
Monitoring and Optimization
- Implementing advanced monitoring tools for AI agent performance
- Detecting model drift and identifying retraining requirements
- Optimizing resource efficiency and system scalability
Security and Governance
- Ensuring compliance with data privacy regulations
- Hardening AI deployment pipelines and API endpoints
- Implementing comprehensive auditing and logging for AI applications
Practical Exercises
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Configuring monitoring for AI performance and resource consumption
Recap and Future Directions
Requirements
- Strong proficiency in Python programming
- Solid understanding of machine learning workflows
- Working knowledge of containerization platforms, specifically Docker
- Practical experience with DevOps methodologies (recommended)
Target Audience
- MLOps Engineers
- DevOps Specialists
14 Hours
