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Course Outline
Foundations of AI Deployment
- Understanding the AI deployment lifecycle
- Navigating challenges in production AI agent rollout
- Core factors: scalability, reliability, and maintainability
Containerization and Orchestration Strategies
- Basics of Docker and containerization principles
- Employing Kubernetes for AI agent orchestration
- Best practices for managing containerized AI applications
Serving AI Models Efficiently
- Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs for AI agent inference
- Managing batch versus real-time prediction scenarios
CI/CD Integration for AI Agents
- Configuring CI/CD pipelines for AI deployments
- Automating the testing and validation of AI models
- Implementing rolling updates and version control management
Monitoring and Performance Optimization
- Deploying monitoring tools to track AI agent performance
- Evaluating model drift and identifying retraining needs
- Enhancing resource efficiency and system scalability
Security, Privacy, and Governance
- Maintaining compliance with data privacy regulations
- Protecting AI deployment pipelines and associated APIs
- Implementing audit trails and logging for AI applications
Practical Lab Exercises
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Configuring monitoring for AI performance and resource consumption
Wrap-up and Future Directions
Requirements
- Strong proficiency in Python programming
- A solid grasp of machine learning workflows
- Familiarity with containerization platforms like Docker
- Exposure to DevOps practices (recommended)
Target Audience
- MLOps engineers
- DevOps specialists
14 Hours
