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 Duration 14 hours

Course Outline

Introduction to AI Inference using Docker

  • Analyzing AI inference workloads
  • Advantages of containerized inference
  • Exploring deployment scenarios and constraints

Creating AI Inference Containers

  • Choosing appropriate base images and frameworks
  • Packaging pretrained models effectively
  • Designing inference code for containerized execution

Protecting Containerized AI Services

  • Reducing the container attack surface
  • Handling secrets and sensitive data securely
  • Implementing safe networking and API exposure strategies

Techniques for Portable Deployment

  • Optimizing images for maximum portability
  • Guaranteeing predictable runtime environments
  • Managing dependencies across different platforms

Local Deployment and Testing

  • Executing services locally via Docker
  • Debugging inference containers
  • Validating performance and reliability

Deploying on Servers and Cloud VMs

  • Adapting containers for remote environments
  • Configuring secure server access
  • Deploying inference APIs on cloud virtual machines

Leveraging Docker Compose for Multi-Service AI Systems

  • Orchestrating inference alongside supporting components
  • Managing environment variables and configurations
  • Scaling microservices using Docker Compose

Monitoring and Maintaining AI Inference Services

  • Applying logging and observability best practices
  • Identifying failures within inference pipelines
  • Updating and versioning models in production environments

Summary and Next Steps

Requirements

  • A solid understanding of fundamental machine learning concepts
  • Proficiency in Python or backend development
  • Familiarity with core containerization concepts

Target Audience

  • Software Developers
  • Backend Engineers
  • Teams responsible for deploying AI services

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