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Course Outline
Introduction to AI Inference with Docker
- Comprehending AI inference workloads.
- Advantages of using containerized inference.
- Deployment scenarios and associated constraints.
Building AI Inference Containers
- Choosing appropriate base images and frameworks.
- Packaging pre-trained models.
- Structuring inference code for container execution.
Securing Containerized AI Services
- Reducing the container attack surface.
- Managing secrets and sensitive files.
- Strategies for secure networking and API exposure.
Portable Deployment Techniques
- Optimizing images for maximum portability.
- Ensuring predictable runtime environments.
- Managing dependencies across different platforms.
Local Deployment and Testing
- Running services locally using Docker.
- Debugging inference containers.
- Assessing performance and reliability.
Deploying on Servers and Cloud VMs
- Adapting containers for remote environments.
- Configuring secure server access.
- Deploying inference APIs on cloud VMs.
Using Docker Compose for Multi-Service AI Systems
- Orchestrating inference with supporting components.
- Managing environment variables and configurations.
- Scaling microservices with Compose.
Monitoring and Maintenance of AI Inference Services
- Logging and observability approaches.
- Detecting failures in inference pipelines.
- Updating and versioning models in production.
Summary and Next Steps
Requirements
- A foundational understanding of machine learning concepts.
- Practical experience with Python or backend development.
- Familiarity with core container concepts.
Target Audience
- Developers.
- Backend engineers.
- Teams responsible for deploying AI services.
14 Hours
Testimonials (2)
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
the trainer had a lot of knowledge and patience to share with us
