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Duration 14 hours
Course Outline
Introduction to AI Inference with Docker
- Comprehending AI inference workloads
- Advantages of containerized inference
- Exploring deployment scenarios and constraints
Creating AI Inference Containers
- Choosing appropriate base images and frameworks
- Packaging pretrained models
- Organizing inference code for container execution
Enhancing the Security of Containerized AI Services
- Reducing the container attack surface
- Handling secrets and sensitive files
- Implementing secure networking and API exposure strategies
Techniques for Portable Deployment
- Optimizing images for portability
- Maintaining predictable runtime environments
- Managing dependencies across different platforms
Local Deployment and Testing
- Executing services locally via Docker
- Troubleshooting inference containers
- Assessing performance and reliability
Deployment on Servers and Cloud VMs
- Adapting containers for remote environments
- Setting up secure server access
- Launching inference APIs on cloud virtual machines
Leveraging Docker Compose for Multi-Service AI Systems
- Coordinating inference with supporting components
- Managing environment variables and configurations
- Scaling microservices using Compose
Monitoring and Maintenance of AI Inference Services
- Adopting logging and observability practices
- Identifying failures in inference pipelines
- Updating and versioning models in production
Wrap-Up and Future Steps
Requirements
- A foundational understanding of basic machine learning concepts
- Practical experience with Python or backend development
- A general familiarity with core container concepts
Target 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.
