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!