Superb DataOps
A place for remarkable teams to create remarkable datasets.
Data quality and distribution are everything when it comes to model performance.
DataOps ensures you always curate, label, and consume the right data - not just more data.
Machine learning is hard So automate the tough parts
Build Better
Current problem
AI requires high-quality training datasets, but teams lack the tools needed to improve and maintain label quality. Most practitioners regularly run into data quality issues, so many projects never make it to production.
How DataOps solves this
Shows you exactly what labels to fix and where to find them, preventing errors before they impact model performance. DataOps provides mislabel detection, which is an automated way of identifying misclassifications in your datasets.
Curate Smater
Current problem
Many teams make ad-hoc decisions about what data to use. But data redundancy and bias lower model performance, and as data volume, velocity, variety, and veracity increase, potential sources of error and imbalance grow exponentially.
How DataOps solves this
Does all the heavy lifting of determining ideal data distribution for you, preventing redundancy and bias from creeping in. DataOps provides test and training set curation, which automates the creation of well-balanced datasets for these purposes.
Build Better
Current problem
Weak scenario performance and model errors that delay production often originate from training data issues. But many teams are blind to what data to collate or label to solve these issues.
How DataOps solves this
Provides representative examples of edge cases to collect or label more of so you can prioritize accordingly. DataOps provides edge case detection, which identifies valuable edge cases within your datasets.
Make data quality
a near-forgone conclusion
DataOps takes the labor, complexity, and guesswork out of data exploration, curation, and quality assurance so you can focus solely on building and deploying the best models.
Uncover and fix mislabels fast
Improve label accuracy by quickly finding and correcting misclassified bounding box and image segmentation annotations. With just a small reference set, mislabel detection analyzes a selected dataset to find suspicious instances that signal something is off, allowing your team to laser-focus their QA efforts.
Automatically curate amazing datasets
Increase model performance at each iteration and optimize time required for model training and development by using more diverse, high-value, and balanced datasets every time. Automated test and training set curation with ideal and realistic data distribution eliminates the ad-hoc data selection practices that negatively impact model performance.
Discover high-value edge cases
Find and mine representative edge cases within your datasets to prioritize for labeling and test/train sets. Edge case detection reduces variance and unpredictability scenarios, allowing you to expand your ML model’s range of training situations and improve on low-performing classes.
Find the right data in seconds
Explore, label, and consume data faster with semantic search. Semantic search converts a reference image to an embedding to return clusters of visually similar images or objects. Natural language queries, combined with data visualization, lets you quickly find images whose embeddings resemble your search query.
Explore and better know your datasets
Visualize datasets in a 2d space with embeddings for each image and annotated regions of interest (ROIs), to fundamentally understand dataset composition and distribution. Embedding store, which includes in-house models that outperform other embedding AI models, allows you to create embeddings for your selected dataset in as little as an hour.