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Course Outline
Introduction to AI Deployment
- Overview of the AI deployment lifecycle
- Challenges associated with deploying AI agents to production
- Key considerations: scalability, reliability, and maintainability
Containerization and Orchestration
- Fundamentals of Docker and containerization
- Orchestrating AI agents using Kubernetes
- Best practices for managing containerized AI applications
Serving AI Models
- Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs for AI agent inference
- Managing batch versus real-time predictions
CI/CD for AI Agents
- Configuring CI/CD pipelines for AI deployments
- Automating the testing and validation of AI models
- Implementing rolling updates and managing version control
Monitoring and Optimization
- Integrating monitoring tools for AI agent performance
- Analyzing model drift and identifying retraining needs
- Optimizing resource utilization and scalability
Security and Governance
- Ensuring compliance with data privacy regulations
- Securing AI deployment pipelines and 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 usage
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Comprehension of machine learning workflows
- Familiarity with containerization tools such as Docker
- Experience with DevOps practices (recommended)
Target Audience
- MLOps Engineers
- DevOps Professionals
14 Hours
