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


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.


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.

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