The landscape of AI infrastructure
Podcast: Practical AIPublished 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. Join the discussionChangelog++ 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, XDaniel Whitenack – Website, GitHub, XShow 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 ★
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