Shorten Development Time

Simplify the development cycle. Empower your AI teams with access to advanced development tools and expertise. 

Book a Demo

There's a Better Way to Build Deep Learning Models.

No more manually developing and optimizing models without any certainty that they will perform well in production. The Deci platform simplifies the processes with powerful tools to quickly develop and deploy highly efficient models. 


Shorter development time on average.


Lower development costs.


Boost in inference perfromance.


Lower training and inference costs.

Book a Demo

Simplify Model Development with Automated Tools

Stop the endless trial & error iterations. Quickly generate fast & efficient architectures with Deci's NAS engine.

Solve Complex Deep Learning Challenges with Ease

Empower your team to quickly resolve development bottlenecks and easily build superior DL-based products with advanced tools.

Eliminate Uncertainty,
Guarantee Success in Production

Deci comes with extensive documentation, tutorials, and dedicated support experts, making it easier to get help when you need it.

"At RingCentral, we strive to provide our customers with the best AI-based experiences. With Deci’s platform, we were able to exceed our deep learning performance goals while shortening our development cycles. Working with Deci allows us to launch superior products faster."

Vadim Zhuk, Senior Vice President R&D


Book a Demo

Build & Deploy Better DL Models, Faster

Deci Platform

Foundation or Custom Models

Choose an ultra performant model or generate a custom one.



Neural Architecture Search Engine

On Prem


Dataset Analyzer

Train or

Use Deci’s library & custom recipe to train on-prem.

On Prem


PyTorch Training Library

Optimize & Run

Apply acceleration techniques. 
Run self-hosted inference anywhere.

On Prem


Optimization & Inference Engine SDK

Add Your Heading Text Here
					from transformers import AutoFeatureExtractor, AutoModelForImageClassification

extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")

model = AutoModelForImageClassification.from_pretrained("microsoft/resnet-50")