What is Dioptra?
Dioptra is a modern, open-source AI infrastructure platform built for data-centric development—empowering teams to systematically curate, understand, and govern training data across computer vision (CV), natural language processing (NLP), and large language model (LLM) workflows. Unlike traditional model-centric tools, Dioptra shifts focus to the data: identifying high-impact unlabeled samples, capturing rich contextual metadata, uncovering hidden failure patterns, and enabling seamless iteration from diagnosis to retraining.
How to use Dioptra?
1. Prioritize unlabeled data using uncertainty, diversity, and domain-gap scoring—surface what truly matters for your model's next leap.
2. Enrich datasets with structured, extensible metadata—track provenance, annotations, confidence scores, and real-world context—without locking data into proprietary silos.
3. Pinpoint *why* models fail: detect distributional shifts, label noise, concept drift, or edge-case blind spots through interpretable, data-driven diagnostics.
4. Deploy intelligent active learning miners—prebuilt and customizable—to continuously recommend optimal samples for labeling based on your current model’s weaknesses.
5. Plug into existing MLOps pipelines via lightweight, RESTful APIs—connect effortlessly with annotation platforms, training orchestrators, and evaluation frameworks.