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:

JupyterDockerPythonGoPostman Where we see AI workflows running:

AWSGCPAzureKubernetes • and KubeFlow • On-prem workstations: NVIDIALambda LabsSystem76 Experimentation / model development:

JupyterLabGoogle ColaboratoryAWS SageMaker • Data Science platforms: DominoDataBricksDataRobotH2O.ai Pipelining and automation:

PachydermAirflowLuigi • Model optimization: OpenVinoTensorRTTensorFlow Lite Serving:

MXNetTensorFlow servingSeldon Monitoring/visibility:

TensorBoardNetronKnock knockPrometheusElasticSearch Upcoming Events:

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