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 – Twitter), GitHub), LinkedIn), Website)

• Daniel Whitenack – Twitter), GitHub), Website)

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!)