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Duration 14 hours
Course Outline
Introduction to AI Inference with Docker
- Comprehending AI inference workloads
- Advantages of containerized inference
- Deployment scenarios and associated constraints
Building AI Inference Containers
- Choosing appropriate base images and frameworks
- Packaging pretrained models
- Organizing inference code for containerized execution
Securing Containerized AI Services
- Reducing the container attack surface
- Handling secrets and sensitive files
- Strategies for safe networking and API exposure
Portable Deployment Techniques
- Optimizing images for enhanced portability
- Guaranteeing predictable runtime environments
- Managing dependencies across different platforms
Local Deployment and Testing
- Executing services locally via Docker
- Troubleshooting inference containers
- Evaluating performance and reliability
Deploying on Servers and Cloud VMs
- Tailoring containers for remote environments
- Setting up secure server access
- Implementing inference APIs on cloud virtual machines
Using Docker Compose for Multi-Service AI Systems
- Orchestrating inference alongside supporting components
- Controlling environment variables and configurations
- Scaling microservices with Compose
Monitoring and Maintenance of AI Inference Services
- Approaches to logging and observability
- Identifying failures within inference pipelines
- Updating and versioning models in production environments
Summary and Next Steps
Requirements
- A foundational grasp of basic machine learning concepts
- Proficiency in Python or backend development
- Basic familiarity with containerization principles
Intended Audience
- Software 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.