Sculpt

The ultimate code-free AI platform

1 follower

The ultimate code-free AI platform

1 follower

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Empower your data science and business teams to drive ROI in your organization. No need to be an AI expert.

Launch tags:Software Engineering•Developer Tools•Artificial Intelligence

Launch Team

Render Sandboxes: a computer for every agentIsolated compute, persistent state and a real filesystem

Promoted

Very cool - could you tell us where Sculpt sits in the space of existing tools and its differentiation?

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7yr ago

Maker

@gobi_dasu Thank you, and thanks for the question. Here's how we look at it: Tools can be divided into: 1) whether they focus on in-house annotators with domain expertise (Us, Prodigy) vs crowdsourced labeling orgs that manage huge teams of annotators (Scale, Figure Eight, Mechanical Turk, etc). In-house teams usually give you an advantage in speed, higher accuracy for more complex/subjective tasks, and security-sensitive tasks. 2) intelligent (use ML to improve the whole process) vs UI that randomly goes over all the data points. Intelligent systems usually reach your desired accuracy faster and cheaper, although it's a delicate balance because it could insert bias or lower the quality of the dataset. We currently see ourselves as an in-house, intelligent data labeling tool. Also here are some of our key advantages in more detail: 1) We've worked hard to optimize the whole process to be very intelligent, time efficient, and customizable. For example: Sculpt uses data augmentation techniques to automatically label thousands of examples, gives active feedback so you stop when you reach the performance you need, lets you examine and edit the features that your model uses to make predictions, etc. 2) We don't charge per annotation, or per API call. We allow you to download the model from Sculpt as a pickled file and deploy it anywhere, and to download the datasets so you can train your own models. 3) Finally, tools like Prodigy also use ML such as active learning to speed up the training process. We agree with their philosophy but we think it's missing key components like active feedback, quality assurance, data augmentation, and giving users control over how the model makes predictions.

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7yr ago

Maker

@gobi_dasu if you have your own views of the space I'd love to hear them out

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7yr ago

@abraham_starosta1 great answer

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7yr ago

This looks super clean and functional -- focused on the pieces you need to get models deployed quickly and smoothly. Keep up the amazing work. What are some features you're looking to build next?

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7yr ago

Maker

@quentin_perrot Thank you Quentin, yes that's what we're going for :) We're currently working on more key functionality for quality assurance such as: - inter-annotator agreement and other annotator statistics - reconciliation of inter-annotator disagreements - more in-depth model error analysis If you have other request please let us know!

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7yr ago

Maker

Hey everyone! Super excited to announce Sculpt's launch! We built Sculpt to accelerate the impact of NLP in real life. Check it out and let us know if you have any thoughts, questions, or feedback!

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7yr ago

Codegen

Huge fan of Sculpt - they really make the data acqusition and inference stack as simple as it can be. Super excited to see them finally breaking out!!

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7yr ago

Maker

@_jayhack_ happy to see Mira doing great!

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7yr ago

Maker

Here's the link to a video showing how to use AI to source ML engineers on LinkedIn:

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7yr ago

Maker

Here's the link to a video showing how to build a text classifier with Sculpt:

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7yr ago

ISSEN

The UI looks cool! How much Ai do you need to know to use it effectively?

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7yr ago

Maker

@mariano_sorgente thank you! you don't need to know AI, I'd say the only thing you need to understand are common model performance metrics like precision and recall

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7yr ago

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