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Course Outline
Introduction to AI Inference with Docker
- Understanding AI inference workloads.
- Benefits of containerized inference.
- Deployment scenarios and constraints.
Building AI Inference Containers
- Selecting base images and frameworks.
- Packaging pretrained models.
- Structuring inference code for container execution.
Securing Containerized AI Services
- Minimizing the container attack surface.
- Managing secrets and sensitive files.
- Safe networking and API exposure strategies.
Portable Deployment Techniques
- Optimizing images for portability.
- Ensuring predictable runtime environments.
- Managing dependencies across platforms.
Local Deployment and Testing
- Running services locally with Docker.
- Debugging inference containers.
- Testing 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 configs.
- 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 solid understanding of basic machine learning concepts.
- Experience with Python or backend development.
- Familiarity with foundational container concepts.
Audience
- Developers.
- Backend engineers.
- Teams responsible for deploying AI services.
14 Hours
Testimonials (3)
multi-tiered, structured course programme.
Bert Paelinckx - Cube SoftwareSolutions
Course - Introduction to Docker
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
The knowledge and exchanges with Augustin
