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Scale RapidThe fastest way to production-quality labels.
Scale StudioLabeling infrastructure for your workforce.
Scale 3D Sensor FusionAdvanced annotations for LiDAR + RADAR data.
Scale ImageComprehensive annotations for images.
Scale VideoScalable annotations for video data.
Scale TextSophisticated annotations for text-based data.
Scale AudioAudio Annotation and Speech Annotation for NLP.
Scale MappingThe flexible solution to develop your own maps.
Scale CatalogCreate, enrich, and enhance eCommerce data.
Scale Enterprise AIModels to support your business use cases.
Scale NucleusThe mission control for your data
Scale LaunchShip and track your models in production
Scale Content UnderstandingManage content for better user experiences
Scale InstantMLNext-day machine learning models, without ML expertise
Scale SpellbookThe platform for large language model apps
Scale SyntheticGenerate synthetic data
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AI Readiness Report 2022
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Scale Data Pipelines
Optimize your data annotation spend by annotating what matters most.
Trusted by the world’s most ambitious AI teams.Meet our customers →
Better together
Improve model performance by identifying points of failure and rare edge cases in Nucleus. Then easily get this data annotated on Rapid or Studio with just one click.
Optimize Spend
Identify the right data to annotate to maximize the ROI of your annotation budget by understanding class imbalances, annotation errors, and edge cases.
Accelerate Development
Get started right away and build a production-quality data pipelines in days, not weeks. Stop waiting for annotations and eliminate manual work when managing your data.
Visualize Data
Visualize and explore your data, annotations and model predictions in one place and never lose track.
Curate for Annotation
Use automation and intelligent filters to effortlessly find the data that will improve your model the most.
Annotate Data
Develop training data with Scale’s trusted workforce – or your own team, leveraging best-in-class ML-assisted tooling.
Enhance Instructions
Leverage the feedback provided by our annotators (or your own) to improve annotation instructions.
Review Annotation Quality
Review all data annotations, compare against current model performance and fix errors with one click.
Manage & Evaluate
Manage and evaluate annotation projects with detailed metrics such as throughput, efficiency, and accuracy.
“Scale Rapid 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.”
Cassandra Ung
Software Engineer, Square
“As Nuro works to ensure efficient deliveries as safely as possible, we depend on tools like Nucleus to curate edge cases which we can use to train ever more accurate and capable models.”
Jack Guo
Head of Autonomy Platform, Nuro
“Properly labeling and counting timber isn't the most common deep learning use case, so we turned to Scale Rapid for our somewhat unique image data labeling needs. Scale's team was able to adapt to our requirements and deliver high-quality labeled data on schedule. Scale Rapid removes the pain and time burden of manually labeling data on a tight timeframe!”
Scott Gregg
CEO and Founder, TimberEye
“Motive encounters all manners of surprising edge cases in real world data collection, so when it comes to knowing we’re labeling the most valuable subset of our collected data, we turn to Scale Nucleus. Its intuitive visualizations, query engine and Autotag help our teams improve both data quality and models, all in the same motion.”
Ali Rehan
Engineering Manager AI/Vision Products, Motive
“Scale Rapid truly stands out from other data labelling solutions in the market. We used it for text categorization and were impressed by the turnaround time and thoughtful design of the workflow for taxonomy creation, quality lab and built-in metrics for auditing along with the other features such as API access, price estimator, etc. which together make it a great self-serve solution.”
Nikhil Raju
Senior ML Engineer, Grata
“Once our data was uploaded, we were able to train, validate and deploy 3 classifiers to our full dataset to identify police cars, ambulances, and firetrucks in just 4 hours, identifying a large number of these rarer case images to send to labeling. This process would have taken weeks without Nucleus.”
Varun Sundar Rabindranath
ML and Perception Engineer, Magna International
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