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Course Outline
Foundations of AI Deployment
- Overview of the AI deployment lifecycle
- Key challenges in transitioning AI agents to production
- Core focus areas: scalability, reliability, and maintainability
Containerization & Orchestration
- Introduction to Docker and fundamental containerization concepts
- Orchestrating AI agents using Kubernetes
- Best practices for overseeing containerized AI applications
Serving AI Models
- Survey of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs for AI agent inference
- Distinguishing between batch and real-time prediction handling
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
Monitoring & Optimization
- Deploying monitoring tools to track AI agent performance
- Detecting model drift and identifying retraining opportunities
- Optimizing resource efficiency and scalability
Security & Governance
- Maintaining compliance with data privacy regulations
- Securing AI deployment pipelines and associated APIs
- Implementing auditing and logging for AI applications
Practical Exercises
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Establishing monitoring for AI performance and resource consumption
Conclusion & Path Forward
Requirements
- Solid proficiency in Python programming
- Clear understanding of machine learning workflows
- Working knowledge of containerization tools, particularly Docker
- Prior experience with DevOps practices (highly recommended)
Target Audience
- MLOps Engineers
- DevOps Professionals
14 Hours
