The landscape of AI infrastructure
The landscape of AI infrastructure  
Podcast: Practical AI
Published On: Tue Apr 02 2019
Description: 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, XDaniel Whitenack – Website, GitHub, XShow Notes:Our locally installed stuff:JupyterDockerPythonGoPostmanWhere we see AI workflows running:AWSGCPAzureKubernetes and KubeFlowOn-prem workstations: NVIDIALambda LabsSystem76Experimentation / model development:JupyterLabGoogle ColaboratoryAWS SageMakerData Science platforms: DominoDataBricksDataRobotH2O.aiPipelining and automation:PachydermAirflowLuigiModel optimization: OpenVinoTensorRTTensorFlow LiteServing:MXNetTensorFlow servingSeldonMonitoring/visibility:TensorBoardNetronKnock knockPrometheusElasticSearchUpcoming Events: Register for upcoming webinars here!