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Duration 14 hours
Course Outline
Foundations of AI Deployment
- Overview of the end-to-end AI deployment lifecycle.
- Addressing the specific challenges of moving AI agents to production.
- Key factors to consider: scalability, reliability, and maintainability.
Containerization and Orchestration Strategies
- Basics of Docker and the principles of containerization.
- Leveraging Kubernetes for the orchestration of AI agents.
- Best practices for the management of containerized AI applications.
Serving AI Models
- Introduction to model serving frameworks (e.g., TensorFlow Serving, TorchServe).
- Developing REST APIs for AI agent inference.
- Distinguishing between and managing batch versus real-time predictions.
CI/CD Pipelines for AI Agents
- Configuring CI/CD pipelines specifically for AI deployments.
- Automation of testing and validation processes for AI models.
- Executing rolling updates and managing version control.
Monitoring and Performance Optimization
- Implementing monitoring tools to track AI agent performance.
- Analyzing model drift and identifying retraining necessities.
- Optimizing resource usage and ensuring scalability.
Security and Governance Frameworks
- Maintaining compliance with data privacy regulations.
- Securing AI deployment pipelines and associated APIs.
- Auditing and logging practices for AI applications.
Practical Application
- Containerizing an AI agent using Docker.
- Deploying an AI agent through Kubernetes.
- Establishing monitoring for AI performance and resource consumption.
Conclusion and Future Directions
Requirements
- Strong proficiency in Python programming.
- A solid grasp of machine learning workflows.
- Working knowledge of containerization technologies, specifically Docker.
- Background in DevOps practices (advised).
Target Audience
- MLOps engineers.
- DevOps specialists.
