Secure & Portable AI Inference with Docker: From Local to Cloud

Prerequisites:

• An understanding of basic machine learning concepts
• Experience with Python or backend development
• Familiarity with foundational container concepts

Audience

• Developers
• Backend engineers
• Teams deploying AI services

Overview:

Docker is a containerization platform used to build portable, isolated, and secure deployment environments for AI inference services.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level technical professionals who wish to build secure, portable AI inference microservices that can be deployed consistently across local machines, servers, or cloud VMs.

By the conclusion of this workshop, participants will be able to:

• Build lightweight inference containers for local and cloud deployment.
• Secure containerized AI services using best-practice techniques.
• Implement portable microservice workflows for consistent environments.
• Deploy AI inference endpoints across diverse infrastructures.

Format of the Course

• Guided lectures paired with practical demonstrations.
• Hands-on exercises to reinforce deployment and security techniques.
• Live-lab practice for building and running portable inference services.

Course Customization Options

• To tailor this training to your infrastructure or AI tooling stack, please contact us to arrange.

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 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