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Course Outline

Introduction to AI Inference with Docker

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

Constructing 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 data
  • Implementing safe networking and API exposure strategies

Techniques for Portable Deployment

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

Local Deployment and Testing

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

Deployment on Servers and Cloud VMs

  • Adapting containers for remote environments
  • Configuring secure server access
  • Launching 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 Compose

Monitoring and Maintaining AI Inference Services

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

Conclusion and Future Steps

Requirements

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

Target Audience

  • Software developers
  • Backend engineers
  • Teams responsible for deploying AI services
 14 Hours

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