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Course Outline

Introduction to AI Inference with Docker

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

Developing AI Inference Containers

  • Choosing appropriate base images and frameworks
  • Packaging pretrained models
  • Structuring inference code for container execution

Securing Containerized AI Services

  • Reducing the container attack surface
  • Handling secrets and sensitive data files
  • Implementing safe networking and API exposure strategies

Portable Deployment Techniques

  • Optimizing images for enhanced portability
  • Ensuring consistent and predictable runtime environments
  • Managing dependencies across different platforms

Local Deployment and Testing

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

Deployment on Servers and Cloud VMs

 
  • Adapting containers for remote environments
  • Configuring secure server access protocols
  • Deploying inference APIs on cloud virtual machines

Utilizing Docker Compose for Multi-Service AI Systems

 
  • Orchestrating inference alongside supporting components
  • Managing environment variables and configuration files
  • Scaling microservices using Compose

Monitoring and Maintenance of AI Inference Services

 
  • Approaches to logging and observability
  • Detecting failures within inference pipelines
  • Updating and versioning models in production environments

Summary and Next Steps

Requirements

  • A foundational understanding of machine learning concepts
  • Practical experience with Python or backend development
  • Familiarity with core containerization principles

Target Audience

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

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  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
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