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Course Outline

Foundations of AI Inference with Docker

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

Creating AI Inference Containers

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

Protecting Containerized AI Services

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

Techniques for Portable Deployment

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

Local Deployment and Testing

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

Deployment on Servers and Cloud VMs

  • Adjusting containers for remote environments
  • Setting up secure server access
  • Deploying inference APIs on cloud VMs

Leveraging Docker Compose for Multi-Service AI Systems

  • Coordinating inference with supporting components
  • Managing environment variables and configurations
  • Scaling microservices using Compose

Monitoring and Maintaining AI Inference Services

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

Wrap-up and Future Directions

Requirements

  • A grasp of fundamental machine learning principles
  • Proficiency with Python or backend development
  • Knowledge of core container concepts

Target Audience

  • Developers
  • Backend engineers
  • Teams responsible for deploying AI services
 14 Hours

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