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Duration 14 hours
Course Outline
Foundations of AI Deployment
- Insight into the AI deployment lifecycle
- Navigating the challenges of transitioning AI agents to production
- Critical factors: scalability, reliability, and maintainability
Containerization and Orchestration Strategies
- Basics of Docker and containerization techniques
- Leveraging Kubernetes for the orchestration of AI agents
- Best practices for overseeing containerized AI applications
AI Model Serving
- Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs for AI agent inference
- Managing batch processing versus real-time predictions
CI/CD Pipelines for AI Agents
- Establishing CI/CD pipelines specifically for AI deployments
- Automation of testing and validation processes for AI models
- Implementing rolling updates and managing version control
Monitoring and Performance Optimization
- Deploying monitoring tools to track AI agent performance
- Evaluating model drift and identifying retraining requirements
- Optimizing resource efficiency and system scalability
Security and Governance Protocols
- Ensuring adherence to data privacy regulations
- Hardening AI deployment pipelines and APIs against threats
- Implementing auditing and logging mechanisms 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
Conclusion and Future Directions
Requirements
- Advanced proficiency in Python programming
- A solid grasp of machine learning workflows
- Familiarity with containerization tools, particularly Docker
- Practical experience with DevOps practices (recommended)
Target Audience
- MLOps Engineers
- DevOps Professionals
