Picsellia provides a complete MLOps platform specifically designed for building, training, monitoring, and deploying computer vision applications. The platform integrates data management, custom labeling tools, experiment tracking, and model monitoring within a unified environment. This allows enterprises to structure visual assets, streamline annotation workflows, and manage the full lifecycle of their deep learning computer vision models efficiently.
Funding
$3.4M 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
Developing computer vision applications requires managing large volumes of visual data, ensuring annotation accuracy, and monitoring model performance, which can be challenging and inefficient. Existing solutions often lack seamless integration across the entire MLOps lifecycle, leading to fragmented workflows and delayed deployments.
Solution
Picsellia provides an end-to-end MLOps platform designed specifically for computer vision, enabling users to efficiently manage, label, train, deploy, and monitor visual data. The platform centralizes unstructured image and video data, offering tools for data exploration, dataset versioning, and collaborative annotation. Picsellia streamlines the MLOps pipeline by integrating experiment tracking, model training, and automated deployment, allowing users to build and deploy high-performing computer vision models with increased speed and accuracy. The platform also offers real-time model monitoring to dissect AI models, identify edge cases, and detect model degradation, facilitating continuous improvement and optimal performance.
Target Audience
Picsellia is designed for ML engineers and data scientists working on computer vision applications across various industries, including construction, waste management, and agriculture.
Features
- Centralized datalake for structuring and organizing visual assets, compatible with various data formats.
- Integrated labeling tool with model-assisted labeling and annotation templates for efficient and precise annotation.
- Experiment tracking and AI laboratory for comparing experiments and building accurate deep-learning models.
- Real-time model monitoring to dissect AI models, identify edge cases, and detect model degradation.
- Automated MLOps pipelines for seamless orchestration of training and deployment processes.
- Serverless deployment options for putting computer vision models into production with 24/7 uptime and pay-as-you-go pricing.
- Integration with existing tools, including training instances, cloud storage, deep learning frameworks, and end-user applications.