Deep Netts

1/10Traction

Deep Netts provides a Java-based deep learning platform that enables developers to build, test, and deploy machine learning models using visual tools and a high-performance Java library. This solution allows organizations to leverage existing Java developer resources for AI integration, reducing the need for specialized expertise and minimizing deployment costs.

HQ unknown

Founded 20224100+ followers

Artificial Intelligence
Developer Tools
Software Only

Updated 5 months ago

Funding

Funding rounds are not available yet.

Founders

1 founder

Zoran Sevarac

CEO

Product

Problem

Developing and deploying machine learning (ML) models often requires specialized AI expertise, leading to increased costs and complexity, especially for organizations primarily using Java. Integrating these models into existing Java applications can be challenging, hindering the adoption of AI in Java-centric environments.

Solution

Deep Netts provides a Java-native platform that simplifies the development, integration, and deployment of AI models. The platform features a visual AI builder with a drag-and-drop interface for data preprocessing, model training, testing, and debugging. It also includes a high-performance, pure Java deep learning library with an intuitive API, enabling seamless integration of AI models into existing Java applications. By utilizing existing Java developer resources, Deep Netts reduces the need for specialized AI expertise and minimizes deployment costs, accelerating AI adoption for Java-based enterprises.

Target Audience

The primary users are Java developers and enterprises looking to integrate AI and machine learning into their existing Java-based applications and workflows.

Features

Visual AI builder with a drag-and-drop interface for simplified model development
Pure Java deep learning library for easy integration into existing Java applications
High-performance implementation of deep learning algorithms
Step-by-step visual expert guide within an integrated environment
Tools for data preprocessing, model training, testing, and debugging
Support for rapid iteration, experiment tracking, and model refinement
Seamless deployment of ML models into Java native environments
Reference implementation of the JSR 381 standard