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

Requirements

  • 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
 14 Hours

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