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Duration 14 hours
Course Outline
Foundations of AI Deployment
- The AI deployment lifecycle at a glance
- Obstacles encountered when moving AI agents to production
- Critical factors: scalability, dependability, and ease of maintenance
Containerization & Orchestration
- Basics of Docker and container technology
- Leveraging Kubernetes for orchestrating AI agents
- Best practices for overseeing containerized AI applications
AI Model Serving
- Introduction to model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Constructing REST APIs for AI agent inference
- Managing batch versus real-time prediction tasks
CI/CD for AI Agents
- Configuring CI/CD pipelines for AI release cycles
- Streamlining the testing and validation of AI models
- Executing rolling updates and overseeing version control
Performance Monitoring & Optimization
- Deploying monitoring solutions to track AI agent performance
- Evaluating model drift and identifying retraining requirements
- Enhancing resource efficiency and scalability
Security & Governance
- Meeting compliance standards for data privacy
- Protecting AI deployment pipelines and API endpoints
- Implementing audit trails and logging for AI systems
Practical Labs
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Setting up performance and resource usage monitoring for AI
Recap & Future Directions
Requirements
- Strong command of Python programming
- Comprehension of machine learning workflows
- Knowledge of containerization tools such as Docker
- Exposure to DevOps methodologies (suggested)
Target Audience
- MLOps engineers
- DevOps specialists
