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Duration 14 hours
Course Outline
Foundations of AI Deployment
- Overview of the AI deployment lifecycle
- Challenges associated with deploying AI agents in production
- Primary factors: scalability, reliability, and maintainability
Containerization and Orchestration
- Basics of Docker and the principles of containerization
- Orchestrating AI agents using Kubernetes
- Best practices for overseeing containerized AI applications
Serving AI Models
- Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Creating 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 version control management
Monitoring and Optimization
- Deploying tools to monitor AI agent performance
- Assessing model drift and determining retraining needs
- Enhancing resource utilization and scalability
Security and Governance
- Maintaining compliance with data privacy regulations
- Hardening AI deployment pipelines and APIs
- 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
Wrap-up and Future Directions
Requirements
- Strong command of Python programming
- Comprehensive understanding of machine learning workflows
- Working knowledge of containerization tools such as Docker
- Practical experience with DevOps methodologies (suggested)
Intended Audience
- MLOps engineers
- DevOps specialists
