About this episode
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!