TrainingData.io application provides high precision training data labeling for Visual AI. Our SaaS solution uses AI-assisted features to give machine learning engineers speed up of several orders of magnitude. Our goal is to "empower data scientists to control quality of training data for their Machine Learning Models"
Who is it for?¶
TrainingData.io's enterprise-ready SaaS solution is designed for machine learning teams that use deep-learning for computer vision.
- Teams that want to accelerate their deep learning training by upto 20X using active learning.
- Teams who understand free open-source tools are good for 1000 images, free open-source tools don't scale for 100,000 images. Teams who believe building data-pipeline is not worth their time.
- Teams who want to use AI to train better AI
- Teams who would rather spend their time in fine tuning hyper-parameters, and serving ML models to their customers.
- Teams who want highest quality training data, who will not compromise on quality.
- Teams who want fast and efficient transition from internal-labeling-teams to outsourced-external-labeling-teams.
Build vs. Rent?¶
Imagine building picture-sharing software-infrastucture for Facebook(TM). Now estimate man-months of R&D effort required for continuous-development & maintenance of such software infrastructure. Building image/video data pipeline can be deceptively resource intensive. To help you decide whether to build or rent a data-pipeline we have prepared a short quiz:
Key Benefits of TrainingData.io's SaaS Solution¶
- High precision labeling tool with Superpixel segmentation with brush-and-eraser, growth-tool, AI-assisted labeling, and active-learning.
- Custom labeling instructions for every task - delivers productivity enhancing user-experience for annotators.
- Data security and privacy protection using On-premises, In-cloud or hybrid hosting. (using Docker and VPN)
- Collaboration with multiple annotator's on same task.
- Annotator performance and Quality Management System (QMS).
- AI-Assisted annotation using existing ML models.
- Out-of-the-box support for medical imaging (DICOM).