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Show HN: NNext.net – A Firebase-like managed vector storage for ML applications

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Re: Show HN: NNext.net – A Firebase-like managed vector storage for ML applications

#11

Post by sitkack »

wakahiu wrote: post #4
phenkdo wrote: post #2

How's this different from Milvus?

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’t want to be encumbered by general software engineering / dev-ops tasks like platform management. They want to focus on data.

> provisioning servers, version upgrades and package installation/ dependencies

If that is nnext's only or main value prop, it will have to be changed.

Re: Show HN: NNext.net – A Firebase-like managed vector storage for ML applications

#12

Post by wakahiu »

sitkack wrote: post #11
wakahiu wrote: post #4

Earlier quoted context omitted.

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’t want to be encumbered by general software engineering / dev-ops tasks like platform management. They want to focus on data.

> provisioning servers, version upgrades and package installation/ dependencies If that is nnext's only or main value prop, it will have to be changed.

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 / defensible?

Re: Show HN: NNext.net – A Firebase-like managed vector storage for ML applications

#13

Post by wakahiu »

malux85 wrote: post #10

I love this idea, but there's no way a managed service like this is going to work, for two reasons: Performance and compliance. Compliance is pretty obvious and even if you don't store the feature dictionaries and only the vectors, that's a hard conversation to have with the compliance team. I GET IT, without the feature dictionaries the vectors are useless, I KNOW this is how it works from a technical point of view,…

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 that require thorough compliance measures are simply not our target audience.

Your second point about performance is critical to almost all of our target audience. First of all, there are self-hosted versions of this such as Facebook’s FAISS (has GPU acceleration), Spotify’s ANNOY, Elastic search KNN search and Milvus. We plan to optimize performance using the latest ANN algorithms, caching, being as close to the wire (Non-REST) transmission of vectors, dimensionally reduction e.t.c. At the end of the day, our stack will probably not be as fast as a self-hosted version but it’ll confer other advantages that we hope ML engineers will find useful similarly to AWS RDS or DynamoDB.

Re: Show HN: NNext.net – A Firebase-like managed vector storage for ML applications

#14

Post by sitkack »

wakahiu wrote: post #12
sitkack wrote: post #11

Earlier quoted context omitted.

> provisioning servers, version upgrades and package installation/ dependencies If that is nnext's only or main value prop, it will have to be changed.

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 / defensible?

Not arguing over managed services, although, they are nearly a yaml file away from being made obsolete.

If there is an OSS system that does everything you do, that isn't a big differentiator. As soon as you have any sort of traction, then the second competitor will swoop in, Lyft, Gitlab, Indiegogo.

You need to have defensible differentiation. Otherwise why not just run the OSS system on K8S and sell the service?

Re: Show HN: NNext.net – A Firebase-like managed vector storage for ML applications

#18

Post by phenkdo »

wakahiu wrote: post #4
phenkdo wrote: post #2

How's this different from Milvus?

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’t want to be encumbered by general software engineering / dev-ops tasks like platform management. They want to focus on data.

My 2c opinion: this might have been true pre-k8s. With k8s, it's become a cinch for anyone to run a scalable system in the cloud. I think you need something more differentiated.

Re: Show HN: NNext.net – A Firebase-like managed vector storage for ML applications

#20

Post by loxias »

phenkdo wrote: post #18
wakahiu wrote: post #4

Earlier quoted context omitted.

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’t want to be encumbered by general software engineering / dev-ops tasks like platform management. They want to focus on data.

My 2c opinion: this might have been true pre-k8s. With k8s, it's become a cinch for anyone to run a scalable system in the cloud. I think you need something more differentiated.

Minor sticking point, I want to object to the prefix "with k8s, it's become a cinch...". In my experience, this prefix is only true when followed by phrases such as "shoot yourself in the foot", "over complicate your infrastructure", &c. ;)
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