AI TCO (Total Cost of Ownership) Considerations from Pilot to Production Scale

Abstract: 

AI projects can start small, from pretty much anywhere. Data scientists work from their laptop, workstation, cloud resources, or from resources within powerful servers and storage in a data center. Fast and reliable results will vary depending on the data, models and the infrastructure resources powering your data ingestion, analysis, model building, training, and optimization. Learn about the TCO considerations as you scale AI from exploratory pilot phases to production.

Bio: 

Justin Emerson is a Principal Technology Evangelist at Pure Storage focused on the FlashBlade product portfolio. He joined Pure in 2020 as a FlashBlade Data Architect for the San Francisco Bay Area. Prior to that, he worked at storage-focused reseller partners for more than a decade.

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

Privacy Settings
We use cookies to enhance your experience while using our website. If you are using our Services via a browser you can restrict, block or remove cookies through your web browser settings. We also use content and scripts from third parties that may use tracking technologies. You can selectively provide your consent below to allow such third party embeds. For complete information about the cookies we use, data we collect and how we process them, please check our Privacy Policy
Youtube
Consent to display content from - Youtube
Vimeo
Consent to display content from - Vimeo
Google Maps
Consent to display content from - Google