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 Duration 14 hours

Course Outline

Introduction to AI Inference with Docker

  • Exploring the nature of AI inference workloads
  • Advantages of containerized inference
  • Reviewing deployment scenarios and associated constraints

Creating AI Inference Containers

  • Choosing appropriate base images and frameworks
  • Package pretrained models efficiently
  • Structuring inference code optimized for container execution

Enhancing the Security of Containerized AI Services

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

Techniques for Portable Deployment

  • Optimizing images to ensure portability
  • Maintaining predictable runtime environments
  • Managing dependencies across different platforms

Local Deployment and Testing Procedures

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

Deployment on Servers and Cloud VMs

  • Adapting containers for remote environments
  • Setting up secure server access
  • Implementing inference APIs on cloud VMs

Leveraging Docker Compose for Multi-Service AI Systems

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

Monitoring and Maintaining AI Inference Services

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

Summary and Recommended Next Steps

Requirements

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

Target Audience

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

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