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.
Join the discussion
Changelog++ members support our work, get closer to the metal, and make the ads disappear. Join today!
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:
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!
★ Support this podcast ★
Nyd den ubegrænsede adgang til tusindvis af spændende e- og lydbøger - helt gratis
Dansk
Danmark