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Duration 14 hours (2 days)
Course Outline
Introduction to AI Deployment
- An overview of the AI deployment lifecycle
- Key challenges associated with moving AI agents to production
- Core factors: scalability, reliability, and long-term maintainability
Containerization and Orchestration
- Fundamentals of Docker and containerization concepts
- Leveraging Kubernetes for the orchestration of AI agents
- Best practices for overseeing containerized AI applications
Serving AI Models
- An introduction to model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Developing REST APIs for AI agent inference
- Managing batch processing versus real-time predictions
CI/CD for AI Agents
- Configuring CI/CD pipelines tailored for AI deployments
- Automating the testing and validation processes for AI models
- Implementing rolling updates and managing version control
Monitoring and Optimization
- Deploying monitoring solutions to track AI agent performance
- Identifying model drift and determining retraining requirements
- Enhancing resource utilization and system scalability
Security and Governance
- Meeting compliance standards for data privacy regulations
- Protecting AI deployment pipelines and associated APIs
- Establishing auditing and logging protocols for AI applications
Hands-On Exercises
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Configuring monitoring for AI performance and resource consumption
Conclusion and Future Steps
Requirements
- Strong proficiency in Python programming
- A solid grasp of machine learning workflows
- Working knowledge of containerization platforms like Docker
- Practical experience with DevOps methodologies (highly recommended)
Target Audience
- MLOps Engineers
- DevOps Specialists
