Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to AI Deployment
- Overview of the AI deployment lifecycle
- Challenges associated with deploying AI agents to production
- Key considerations: scalability, reliability, and maintainability
Containerization and Orchestration
- Foundations of Docker and containerization
- Utilizing Kubernetes for AI agent orchestration
- Best practices for managing containerized AI applications
Serving AI Models
- Overview of model serving frameworks (e.g., TensorFlow Serving, TorchServe)
- Creating REST APIs for AI agent inference
- Managing batch versus real-time predictions
CI/CD for AI Agents
- Configuring CI/CD pipelines for AI deployments
- Automating the testing and validation of AI models
- Executing rolling updates and managing version control
Monitoring and Optimization
- Implementing monitoring tools to track AI agent performance
- Analyzing model drift and identifying retraining needs
- Optimizing resource utilization and scalability
Security and Governance
- Ensuring adherence to data privacy regulations
- Securing AI deployment pipelines and APIs
- Auditing and logging for AI applications
Practical Exercises
- Containerizing an AI agent using Docker
- Deploying an AI agent via Kubernetes
- Setting up monitoring for AI performance and resource consumption
Summary and Future Directions
Requirements
- Strong proficiency in Python programming
- A solid understanding of machine learning workflows
- Familiarity with containerization tools, such as Docker
- Experience with DevOps practices (recommended)
Target Audience
- MLOps engineers
- DevOps professionals
14 Hours
