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 in deploying AI agents to production
- Key considerations: scalability, reliability, and maintainability
Containerization and Orchestration
- Introduction to Docker and containerization basics
- Using 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)
- Building REST APIs for AI agent inference
- Handling batch vs real-time predictions
CI/CD for AI Agents
- Setting up CI/CD pipelines for AI deployments
- Automating testing and validation of AI models
- Rolling updates and managing version control
Monitoring and Optimization
- Implementing monitoring tools for AI agent performance
- Analyzing model drift and retraining needs
- Optimizing resource utilization and scalability
Security and Governance
- Ensuring compliance with data privacy regulations
- Securing AI deployment pipelines and APIs
- Auditing and logging for AI applications
Hands-On Activities
- Containerizing an AI agent with Docker
- Deploying an AI agent using Kubernetes
- Setting up monitoring for AI performance and resource usage
Summary and Next Steps
Requirements
- Proficiency in Python programming
- Understanding of machine learning workflows
- Familiarity with containerization tools like Docker
- Experience with DevOps practices (recommended)
Audience
- MLOps engineers
- DevOps professionals
14 Hours
Testimonials (1)
Trainer responding to questions on the fly.
