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Course Outline

Introduction to AI Inference using Docker

  • Comprehending AI inference workloads.
  • The advantages of containerized inference.
  • Deployment scenarios and associated constraints.

Constructing AI Inference Containers

  • Choosing appropriate base images and frameworks.
  • Packaging pretrained models effectively.
  • Organizing inference code for containerized execution.

Securing 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 ease of portability.
  • Guaranteeing 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 virtual machines.

Leveraging Docker Compose for Multi-Service AI Systems

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

Monitoring and Maintenance of AI Inference Services

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

Wrap-Up and Next Steps

Requirements

  • A solid grasp of fundamental machine learning concepts.
  • Practical experience with Python or backend development.
  • Basic familiarity with core container concepts.

Target Audience

  • Software Developers
  • Backend Engineers
  • Teams responsible for deploying AI services
 14 Hours

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