Cdeeply provides an automated platform that creates, trains, and hosts neural network models for tabular data with just a few configuration steps. Users can generate models for prediction, nonlinear regression, or autoencoding, and access them via simple API endpoints, eliminating the need for manual architecture design and hyperparameter tuning.
Funding
Funding not disclosed
Founders
Product
Problem
Creating and training neural networks for tabular datasets often requires extensive coding, hyperparameter tuning, and domain expertise, which slows down development and limits accessibility for many teams.
Solution
Cdeeply offers an automated platform that generates neural network models tailored to tabular data with a few configuration steps. Users can select from prediction, nonlinear regression, or autoencoding tasks, and the service builds, trains, and hosts the model as an API endpoint. The generated models handle vector‑to‑vector mappings, enabling both forward predictions and data compression without manual architecture design. Results are returned via simple API calls, allowing integration into existing applications and workflows. By abstracting model creation and deployment, Cdeeply reduces the time and expertise needed to apply deep learning to structured data.
Target Audience
Primary customers are developers, data scientists, and product teams that need fast, reliable deep‑learning models for structured data without building custom pipelines.
Features
- One‑click generation of neural networks for tabular prediction, regression, and autoencoding
- Automatic hyperparameter selection and training on user‑provided datasets
- Hosted API endpoints for real‑time inference and data encoding
- Built‑in support for data compression and generation using autoencoder architectures
- Scalable cloud infrastructure that manages model hosting and scaling