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Duration 14 hours
Course Outline
Foundations of AI Deployment
- An overview of the end-to-end AI deployment lifecycle
- Addressing the complexities of moving AI agents into production
- Core strategic factors: scaling, dependability, and long-term maintenance
Containerization & Orchestration Strategies
- Basics of Docker and containerization concepts
- Harnessing Kubernetes for the orchestration of AI agents
- Best practices for sustaining containerized AI workloads
AI Model Serving Architectures
- Review of popular model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs to facilitate AI agent inference
- Managing the distinction between batch processing and real-time prediction needs
CI/CD for AI Workflows
- Configuring CI/CD pipelines specifically for AI deployment cycles
- Automating the testing and validation stages for AI models
- Executing rolling updates and overseeing version control
Performance Monitoring & Tuning
- Integrating monitoring solutions to track AI agent performance
- Identifying model drift and determining retraining requirements
- Enhancing resource efficiency and system scalability
Security Frameworks & Governance
- Compliance management regarding data privacy regulations
- Hardening AI deployment pipelines and associated APIs
- Implementing comprehensive audit trails and logging for AI systems
Practical Workshops
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Configuring monitoring tools for performance and resource metrics
Wrap-Up & Future Directions
Requirements
- Strong command of Python programming
- Solid grasp of machine learning workflows
- Working knowledge of containerization technologies such as Docker
- Practical experience with DevOps methodologies (advisable)
Target Learners
- MLOps specialists
- DevOps engineers
