The landscape of AI infrastructure

The landscape of AI infrastructure

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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!


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