The landscape of AI infrastructure

Being that this is “practical” AI, we decided that it would be good to take time to discuss various aspects of AI infrastructure. In this full-connected episode, we discuss our personal/local infrastructure along with trends in AI, including infra for training, serving, and data management.

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Featuring:

• Chris Benson – Website, GitHub, LinkedIn, X
• Daniel Whitenack – Website, GitHub, X

Show Notes:

Our locally installed stuff:

• Jupyter


• Docker


• Python


• Go


• Postman

Where we see AI workflows running:

• AWS


• GCP


• Azure


• Kubernetes and KubeFlow


• On-prem workstations:
  
  
  
  • NVIDIA
  
  
  • Lambda Labs
  
  
  • System76
  
  
  
  

Experimentation / model development:

• JupyterLab


• Google Colaboratory


• AWS SageMaker


• Data Science platforms:
  
  
  
  • Domino
  
  
  • DataBricks
  
  
  • DataRobot
  
  
  • H2O.ai
  
  
  
  

Pipelining and automation:

• Pachyderm


• Airflow


• Luigi


• Model optimization:
  
  
  
  • OpenVino
  
  
  • TensorRT
  
  
  • TensorFlow Lite
  
  
  
  

Serving:

• MXNet


• TensorFlow serving


• Seldon

Monitoring/visibility:

• TensorBoard


• Netron


• Knock knock


• Prometheus


• ElasticSearch

Something missing or broken? PRs welcome!