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 Duration 14 hours

Course Outline

Introduction to AI Inference with Docker

  • Comprehending AI inference workloads
  • Advantages of containerized inference
  • Deployment scenarios and associated constraints

Building AI Inference Containers

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

Securing Containerized AI Services

  • Reducing the container attack surface
  • Handling secrets and sensitive files
  • Strategies for safe networking and API exposure

Portable Deployment Techniques

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

Local Deployment and Testing

  • Executing services locally via Docker
  • Troubleshooting inference containers
  • Evaluating performance and reliability

Deploying on Servers and Cloud VMs

  • Tailoring containers for remote environments
  • Setting up secure server access
  • Implementing inference APIs on cloud virtual machines

Using Docker Compose for Multi-Service AI Systems

  • Orchestrating inference alongside supporting components
  • Controlling environment variables and configurations
  • Scaling microservices with Compose

Monitoring and Maintenance of AI Inference Services

  • Approaches to logging and observability
  • Identifying failures within inference pipelines
  • Updating and versioning models in production environments

Summary and Next Steps

Requirements

  • A foundational grasp of basic machine learning concepts
  • Proficiency in Python or backend development
  • Basic familiarity with containerization principles

Intended Audience

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

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