PerceptiLabs offers a visual modeler for TensorFlow that simplifies the process of building, training, and deploying machine learning models. The platform enhances productivity by providing structured workflows, real-time model evaluation, and seamless integration for deployment, enabling users to efficiently manage their MLOps processes.
Funding
$2.3M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Building, training, and deploying deep learning models can be a complex and time-consuming process, often requiring extensive coding and specialized expertise. Traditional methods lack intuitive interfaces for visualizing model architecture and performance, hindering rapid iteration and optimization.
Solution
PerceptiLabs provides a visual modeling tool designed to simplify the development lifecycle for TensorFlow-based deep learning models. The platform offers a drag-and-drop interface, enabling users to construct model architectures by connecting pre-built components. It automatically generates TensorFlow code and provides real-time visualizations of data transformations and model metrics, facilitating rapid experimentation and debugging. PerceptiLabs streamlines MLOps workflows by separating modeling and training processes, allowing users to quickly evaluate different model configurations and optimize hyperparameters. The tool supports model deployment to both cloud and edge environments, offering flexibility for various production scenarios.
Target Audience
The primary target audience includes machine learning engineers, data scientists, and developers who use TensorFlow and seek a more intuitive and efficient way to build, train, and deploy deep learning models.
Features
- Visual drag-and-drop interface for constructing deep learning models
- Automatic TensorFlow code generation from visual models
- Real-time visualizations of data transformations and model performance metrics
- Pre-built components for common deep learning layers and operations
- Hyperparameter auto-generation and tuning capabilities
- Integrated model evaluation and benchmarking tools
- Support for exporting models in various formats for cloud and edge deployment
- Integration with Gradio for simplified model deployment