Comments on: Tecton Moves Mountains Of Data To AI Models Automagically https://www.nextplatform.com/2024/09/25/tecton-moves-mountains-of-data-to-ai-models-automagically/ In-depth coverage of high-end computing at large enterprises, supercomputing centers, hyperscale data centers, and public clouds. Wed, 09 Oct 2024 16:43:31 +0000 hourly 1 https://wordpress.org/?v=6.7.1 By: Slim Albert https://www.nextplatform.com/2024/09/25/tecton-moves-mountains-of-data-to-ai-models-automagically/#comment-235691 Wed, 25 Sep 2024 23:34:42 +0000 https://www.nextplatform.com/?p=144749#comment-235691 Swell interview, on a topic I hadn’t quite thought about beyond graph and vector databases! It looks like Tecton has a solid and important niche setup for itself, focusing on heterogeneous data orchestration (control, normalization, enumeration, streaming, …) for AI modeling (training and inference), with a precise expertise and focus, that prevents the “jack of all trades, master of none” syndrome which could affect competing “overly ambitious” frameworks (a bit like the “Do One Thing And Do It Well” design philosophy that is behind UNIX programs).

I like De Blaso’s advices to “start with the simple thing”, and “Just get one of these things up and running, and then we know that we have a path to success”, especially with the idea of having multiple small AI models each doing something rather specialized, and doing it well, from a common orchestrated heterogeneous data pool. The main challenge I guess is ensuring security, privacy, and maybe secrecy as well, when the system is cloud-based vs on-prem or co-located.

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