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Duration 14 hours
Course Outline
Introduction to AI Inference using Docker
- Comprehending AI inference workloads
- Advantages of containerized inference
- Deployment scenarios and associated constraints
Constructing AI Inference Containers
- Choosing appropriate base images and frameworks
- Packaging pre-trained models effectively
- Organising inference code for efficient container execution
Securing Containerised AI Services
- Reducing the container attack surface
- Handling secrets and sensitive files securely
- Strategies for safe networking and API exposure
Techniques for Portable Deployment
- Optimising images to enhance portability
- Guaranteeing predictable runtime environments
- Managing dependencies consistently across platforms
Local Deployment and Testing
- Executing services locally via Docker
- Troubleshooting inference containers
- Evaluating performance and reliability
Deployment on Servers and Cloud VMs
- Adapting containers for remote environments
- Setting up secure server access
- Deploying inference APIs on cloud VMs
Utilising Docker Compose for Multi-Service AI Systems
- Coordinating inference with supporting components
- Managing environment variables and configurations
- Scaling microservices using Compose
Monitoring and Maintaining AI Inference Services
- Approaches to logging and observability
- Identifying failures within inference pipelines
- Updating and versioning models in production environments
Wrap-up and Future Directions
Requirements
- A solid grasp of fundamental machine learning concepts
- Practical experience with Python or backend development
- Basic familiarity with core container concepts
Intended Audience
- Developers
- Backend engineers
- Teams responsible for deploying AI services
Testimonials (2)
As i said before , for a person like me (no exp. ) this was a gateway to understanding features and functions with these programs/tools & etc. .
Patrick V. Duylovski - UBB + DZI (KBC GROUP)
Course - Docker and Kubernetes
multi-tiered, structured course programme.
