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 Duration 14 hours

Course Outline

Foundations of AI Inference with Docker

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

Developing AI Inference Containers

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

Enhancing Security for Containerized AI Services

  • Reducing the container attack surface
  • Handling secrets and sensitive data
  • Strategizing safe networking and API exposure

Techniques for Portable Deployment

  • Refining images for maximum portability
  • Guaranteeing predictable runtime environments
  • Handling dependencies across multiple platforms

Local Deployment and Verification

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

Deployment to Servers and Cloud VMs

  • Tailoring containers for remote settings
  • Establishing secure server access
  • Implementing inference APIs on cloud VMs

Leveraging Docker Compose for Multi-Service AI Systems

  • Coordinating inference with auxiliary components
  • Controlling environment variables and configurations
  • Scaling microservices using Compose

Oversight and Upkeep of AI Inference Services

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

Wrap-up and Future Directions

Requirements

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

Intended Audience

  • Software developers
  • Backend engineers
  • Teams responsible for AI service deployment

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