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	Podcast:		Practical AI
	Episode:		The landscape of AI infrastructure
	Category:		Technology
	Duration:		00:51:33
	Publish Date:		2019-04-02 06:00:00
	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. Discuss on Changelog News 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. 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. GitPrime – Download GitPrime’s 20 Patterns book, a field guide to help engineering managers recognize achievement, spot bottlenecks, and debug development processes with data. 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 – Twitter, GitHub, Website Daniel Whitenack – Twitter, GitHub, Website Notes and Links 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
	Total Play:		0