Better data leads to more performant models. Performant models lead to faster deployment. Deliver value from your AI investments faster with better data.
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Scale’s mission is to accelerate the development of artificial intelligence. We do this by providing a data-centric, end-to-end solution to manage the entire ML lifecycle.
Retrieve human insights for search relevance, ecommerce, natural language processing, audio transcription, document processing and more. Operational excellence augmented by technology enables us to exceed demanding quality, cost, and latency requirements.Learn More →
Annotate large volumes of 3D sensor, image, and video data at high throughput. ML-powered pre-labeling and an automated quality assurance system ensure high quality annotations for the most safety critical applications.Contact Sales →
“OpenAI threw a bunch of tasks at Scale AI with difficult characteristics, including tight latency requirements and significant ambiguity in correct answers. In response, Scale worked closely with us to adjust their QA systems to our needs.”
Quickly choose what data to label with active learning and advanced querying. Visualize data, identify edge cases with integrated model predictions, and solve the long tail with Scale Nucleus.Explore Scale Nucleus →
Achieve robust document understanding and extraction across any document type. Pre-trained but fine-tuned with your data to your exact use case, Scale Document guarantees 99%+ quality and low latency to reduce costs up to 90%+ with an optional human-in-the-loop review.Learn How →
Upload predictions to Nucleus via API. Track model performance, compare model runs, sort failure examples by metrics of interest, and build model unit tests out of curated dataset slices to catch regressions in key scenarios.Learn How →
Collect and generate representative text and audio data in 50+ languages across 70+ countries. Data collection workflows are API supported and seamlessly integrate with Scale’s data labeling pipeline. Image and Video collection coming soon.
Augment ground-truth training data with infinite varieties of synthetic data and expose your model to more data than you can otherwise collect. Confidently develop generalizable ML models by understanding how they will react to rare or dangerous real-world scenarios before you encounter them in production.