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Duration 14 hours
Course Outline
Introduction to AI Inference with Docker
- Comprehending AI inference workloads
- Advantages of containerized inference
- Deployment contexts and limitations
Constructing AI Inference Containers
- Choosing appropriate base images and frameworks
- Packaging pretrained models
- Organizing inference code for containerized execution
Protecting Containerized AI Services
- Reducing the container attack surface
- Handling secrets and sensitive files
- Strategies for secure networking and API exposure
Techniques for Portable Deployment
- Optimizing images for portability
- Guaranteeing consistent runtime environments
- Handling dependencies across different platforms
Local Deployment and Testing
- Executing services locally via Docker
- Debugging inference containers
- Evaluating performance and reliability
Deploying on Servers and Cloud VMs
- Adapting containers for remote environments
- Setting up secure server access
- Deploying inference APIs on cloud VMs
Leveraging Docker Compose for Multi-Service AI Systems
- Coordinating inference with auxiliary components
- Managing environment variables and configurations
- Scaling microservices using Compose
Monitoring and Maintenance of AI Inference Services
- Logging and observability methodologies
- Identifying failures in inference pipelines
- Updating and versioning models in production
Summary and Next Steps
Requirements
- Fundamental knowledge of machine learning concepts
- Practical experience with Python or backend development
- Basic familiarity with containerization principles
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.
