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, XDaniel Whitenack – Website, GitHub, XShow 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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