Introducing Aquarium Learning: The Ultimate Platform for ML Data Curation

Aquarium Learning revolutionizes machine learning data curation by leveraging advanced embedding technology to identify and address critical model performance issues. Trusted by world-class ML teams, Aquarium empowers users to improve their models 10x faster. Key features include:

• Embeddings Made Easy: Seamlessly harness the power of neural network embeddings without infrastructure hassles.
• Surface Critical Errors: Automatically pinpoint the most significant patterns of model failures in your dataset.
• Targeted Data Collection: Efficiently trawl through vast unlabeled datasets to uncover edge case scenarios, enabling rapid bootstrapping of new classes.

Aquarium Learning supports various data types, including classification, 2D and 3D object detection, and segmentation. With scalability, reliability, and robust data security measures, Aquarium is the go-to solution for enterprises. Experience hands-on support and solutions engineering resources to ensure your success.

Unlock the full potential of your ML models with Aquarium Learning. Get in touch or try the demo today!

Embeddings Made Easy

This feature allows users to leverage neural network embeddings without the hassle of maintaining infrastructure or debugging embedding models. It simplifies the process of embedding, which is crucial for understanding and improving model performance. By abstracting away the complexities, users can focus more on improving their models rather than managing technical details.

Surface Critical Errors

This function automatically identifies the most critical patterns of model failures in datasets. It helps users understand the long tail of edge cases and prioritize which issues to address first. This is invaluable for improving model accuracy and reliability, as it targets the most significant errors that can have the biggest impact on performance.

Targeted Data Collection

This feature enables users to trawl through massive unlabeled datasets to find edge case scenarios. It uses few-shot learning technology to bootstrap new classes with just a handful of examples. This is particularly useful for expanding the scope of models without requiring extensive manual labeling, thereby speeding up the data curation process.

Scalability and Reliability

Aquarium is designed to handle large datasets, scaling reliably to hundreds of millions of data points. This ensures that as users' datasets grow, the platform can continue to provide valuable insights and support without performance degradation. This scalability is essential for enterprises dealing with vast amounts of data.

Data Security

Aquarium offers robust data security features, including SOC2 Type 2 certification and Anonymous Mode. These features ensure that sensitive data remains protected, which is critical for organizations that handle confidential information. The security measures also build trust with users, knowing that their data is safe.

Summary

Aquarium Learning is a comprehensive platform designed to accelerate the curation and management of datasets for machine learning models. It offers a range of features that simplify embedding, identify critical errors, facilitate targeted data collection, ensure scalability and reliability, and provide robust data security. These features collectively enable world-class ML teams to improve their models 10x faster, making it an indispensable tool in the AI development process.