_fudu

5 ms·

1. 1.▲
   by _fudu 6y ago

   You bring up two viable points of view. Compliance is hard to get around. There are products that have done this by sheer force of their cyber security budgets such as large cloud providers which are HIPAA compliant. As a result, companies
2. 2.▲
   by _fudu 6y ago

   Entire companies are built upon providing fast, managed services - Algolia, Firebase and Heroku to name but a few. Is your point that the market is simply too small/doesn’t exist or that the value prop is simply too weak / defensi
3. 3.▲
   by _fudu 6y ago

   This is really cool. Thanks for the link.
4. 4.▲
   by _fudu 6y ago

   How would you handle an online ML application where the set of embeddings is changing such as an image recognition app where images are constantly being added and need to be deduplicated?
5. 5.▲
   by _fudu 6y ago

   Exactly, once you have latency-sensitive applications that read-write embeddings, then you have to rethink your vector storage system. Furthermore, when your dataset grows to millions of datapoints, having a system that scales according avo
6. 6.▲
   by _fudu 6y ago

   Main thing is that NNext is fully managed - you don’t have to worry about provisioning servers, version upgrades and package installation/ dependencies. One of the main thing I’ve observed about ML engineers is that they typically don’
7. 7.▲
   Show HN: NNext.net – A Firebase-like managed vector storage for ML applications

   37 points by _fudu 6y ago|23 comments