Collect, curate, and annotate data. Train models and evaluate. Repeat.
The Best In The Business
The Scale Data Engine is trusted by the world’s leading ML teams to accelerate the development of their models. The scale of our operations, experts and quality is unmatched in the industry.
Scale can provide the core tenet of any dataset with high-quality labels from domain experts.
Easily find, categorize, and fix model failures with Scale’s Data Engine. Then, optimize labeling spend with high-value curated data.
Scale's data engine can support any ML project from lower-volume experiments to high-volume production projects. Scale up, or down, as needed.
Scale delivers the greatest variety and diversity of data to help deliver the greatest value to your model performance.
Scale Data Engine
For AI teams, Scale Data Engine improves your models by improving your data.
Unearth the most valuable data by intelligently managing your dataset
Scale’s suite of dataset management, testing, model evaluation, and model comparison tools enable you to “label what matters.” Maximize the value of your labeling budget by identifying the highest value data to label, even without ground truth labels.
The One-Stop-Shop For Building AI
Data engine is the process of improving machine learning models with high quality, diverse and large datasets powered by experts. Unlock model performance with the Scale Data Engine.
After initial pre-training, create complex prompt-response pairs from scratch.
Apply human preferences to model outputs.
Use prompt injection techniques to find vulnerabilities.
Evaluate your model against a set of complex and diverse prompts to find weak points.
Supported Annotation Types
Scale 3D Sensor Fusion
Learn More About The Data Engine
Why Is ChatGPT So Good?
OpenAI applied reinforcement learning with human feedback (RLHF) to enhance ChatGPT. Understand the role RLHF plays in enhancing large language models and how to implement it.
Guide to Data Annotation
The success of ML models is dependent on data and label quality. Read our authoritative guide to ensure you get the highest quality labels.
Guide: Computer Vision
Computer Vision focuses on developing systems that can process, analyze, and make sense of visual data. Read our guide to learn about how it works and top use cases by industry.
Guide: Training & Building Models
ML models take data as inputs and deliver a classification, a prediction, or some other indicator as an output. Read our guide to learn more about how to train models.
One of the things we love about Scale is the fact that we can fully label the world. We can label 2D bounding boxes, 3D bounding boxes, but also semantic segmentation, including in 3D, to understand as much as possible, including scenarios we don’t foresee today.
Machine Learning Lead, Toyota Research Institute
Scale has made it easier for us to gather annotations at a good price point. The UI is simple to navigate, and the built in worker evaluation pipeline and batch options saves us time and helps enforce best practices so that we can get high-quality training data.
Software Engineer, Square
“ML models only deliver the highest accuracy when they can handle edge cases that might be challenging, uncommon, or even dangerous. The Autotag functionality in Data Engine: Dataset Management helps us immensely by identifying examples of infrequent scenarios in our dataset, all with a simple query. As Nuro works to ensure efficient deliveries as safely as possible, we depend on tools like Scale Data Engine: Dataset Management to curate edge cases which we can use to train ever more accurate and capable models.”
Head of Autonomy Platform, Nuro
“After training for years to do this research, it was frustrating how much time I was spending just annotating data. Working with Scale Rapid freed up my time to work on the parts of research that require my expertise.”
Neuroscience Post-Doc, Harvard Medical School
Scale already provided quality annotations to our perception team, so it was a natural extension to use their platform and solve adjacent pipeline problems of data selection and model performance debugging. The powerful search capabilities and easy-to-use tools made it easy for us to get started with our existing library of annotations.
Sr. Manager, Data Operations, Velodyne LiDAR