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Course Outline
Foundations of AI Deployment
- Exploring the AI deployment lifecycle
- Navigating challenges associated with production AI agent deployment
- Key factors: scalability, reliability, and long-term maintainability
Containerization and Orchestration Strategies
- Basics of Docker and containerization concepts
- Orchestrating AI agents using Kubernetes
- Best practices for handling 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 prediction workloads
CI/CD Pipelines for AI Agents
- Configuring CI/CD pipelines for AI deployment
- Automating model testing and validation processes
- Implementing rolling updates and version control management
Performance Monitoring and Optimization
- Deploying tools to monitor AI agent performance
- Evaluating model drift and identifying retraining needs
- Optimizing resource efficiency and scalability
Security and Governance Frameworks
- Complying with data privacy regulatory standards
- Hardening AI deployment pipelines and API endpoints
- Implementing auditing and logging for AI applications
Practical Application Modules
- Containerizing an AI agent using Docker
- Deploying AI agents via Kubernetes
- Establishing monitoring for AI performance and resource consumption
Recap and Future Directions
Requirements
- Strong proficiency in Python programming
- A solid grasp of machine learning workflows
- Working knowledge of containerization tools such as Docker
- Practical experience with DevOps methodologies (recommended)
Target Audience
- MLOps engineers
- DevOps specialists
14 Hours
