[_mdb]

CEO of Tecton here, and happy to give more context. Tecton is specifically focused on solving a few key data problems to make it easier to deploy and manage ML in production. e.g.:

- How can I deliver these features to my model in production?

- How do I make sure the data I'm serving to my model is similar to what is trained on?

- How can I construct my training data with point in time accuracy for every example?

- How can I reuse features that another DS on my team built?

We've found that there's a ton of complexity getting data right for real-time production use cases. These problems can be solved, but require a lot of care and are hard to get right. We're building production-ready feature infrastructure and managed workflows that "just work" for teams that can’t or don’t want to dedicate large engineering teams to these problems.

At the core of Tecton is a managed feature store, feature pipeline automation, and a feature server. We’re building the platform to integrate with existing tools in the ML ecosystem.

We’re going to share more about the platform in the next few months. Happy to answer any questions. I’d also love to hear what challenges folks on this thread have encountered when putting ML into production.

[bogomipz]

All of the open positions listed on your careers page appear to be broken. There is no field to upload or attach a CV when applying to any of the roles. Also why would a LinkedIn Profile be mandatory in order to apply for a role? There are many qualified people who have simply chosen not to be a part of that social network.

[_mdb]

Ah. We're on it. LinkedIn shouldn't be required. Thanks for flagging.