We got early access to the Steamship API and it's been awesome so far. Worth checking out if you want to add NLP to your application but don't have the expertise in-house.
OP here. We're really excited about what we've been building and eager to get your feedback.
We think developer usability is one of the biggest bottlenecks in AI today. It's easy to make an AI demo, but 100x harder to scale that into production. You end up adopting a mess of spaghetti infrastructure.
We took a lot of inspiration from the way Heroku, Vercel, and Netlify simplified the prototype -> production pathway for web app development, and we're building a platform to do that for language AI.
Today we're launching three packages built on top of Steamship, but soon we'll have the whole SDK up and running for folks to build with.
Our dream is targeted, full-lifecycle AI that you can use with the easy of NPM or PyPi.
Congrats on the launch!
I've seen the spaghetti code you talk about many times while building NLP, so I would love to try your product out. When will the SDK be available to the public?
And yes, the spaghetti code is a huge issue behind the scenes of production NLP deployments. We really hope this can make a huge dent in the problem.
Right now we're aiming for self-service signups in October and then the first SDK launch in November. If you've got particular needs/applications you're interested in, I'd love to hear! Feel free to reach out any time at ted@steamship.com.
Congrats on the launch! Does all this run in Steamship's own AWS cloud, or on the customer's own private AWS cloud? Perhaps the latter is a "premium" feature.
And I assume AWS is completely dominant in this space? I.e. no Azure or Google Cloud...
We're built atop AWS at the moment, but we're designed in a way that's friendly to cloud-agnostic / private-VPC usage down the road if that becomes critical.
For now, we're focused on public API endpoints in which we shard data across different "workspaces" in the background. Each workspace is a combination of stateful data (relations, vectors, binary), infrastructure (models), and plugins (connections to other services).
When you import & use a Steamship Package, you pass it an identifier that binds it to the particular workspace you want to operate out of.
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[ 2.7 ms ] story [ 32.4 ms ] threadOP here. We're really excited about what we've been building and eager to get your feedback.
We think developer usability is one of the biggest bottlenecks in AI today. It's easy to make an AI demo, but 100x harder to scale that into production. You end up adopting a mess of spaghetti infrastructure.
We took a lot of inspiration from the way Heroku, Vercel, and Netlify simplified the prototype -> production pathway for web app development, and we're building a platform to do that for language AI.
Today we're launching three packages built on top of Steamship, but soon we'll have the whole SDK up and running for folks to build with.
Our dream is targeted, full-lifecycle AI that you can use with the easy of NPM or PyPi.
And yes, the spaghetti code is a huge issue behind the scenes of production NLP deployments. We really hope this can make a huge dent in the problem.
Right now we're aiming for self-service signups in October and then the first SDK launch in November. If you've got particular needs/applications you're interested in, I'd love to hear! Feel free to reach out any time at ted@steamship.com.
And I assume AWS is completely dominant in this space? I.e. no Azure or Google Cloud...
We're built atop AWS at the moment, but we're designed in a way that's friendly to cloud-agnostic / private-VPC usage down the road if that becomes critical.
For now, we're focused on public API endpoints in which we shard data across different "workspaces" in the background. Each workspace is a combination of stateful data (relations, vectors, binary), infrastructure (models), and plugins (connections to other services).
When you import & use a Steamship Package, you pass it an identifier that binds it to the particular workspace you want to operate out of.