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.
Sponsors:
DigitalOcean • – Check out DigitalOcean’s dedicated vCPU Droplets with dedicated vCPU threads. • Get started for free with a $100 credit. Learn more at do.co/changelog • . DataEngPodcast • – A podcast about data engineering and modern data infrastructure. Fastly • – Our bandwidth partner. • Fastly powers fast, secure, and scalable digital experiences. Move beyond your content delivery network to their powerful edge cloud platform. Learn more at fastly.com • . Rollbar • – We move fast and fix things because of Rollbar. • Resolve errors in minutes. Deploy with confidence. Learn more at rollbar.com/changelog • .
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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