Use case

Improve Machine Learning Robustness with Synthetic Data

Augment ML training datasets with Gretel to improve performance and ensure safety across the MLOps lifecycle.
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Challenge

The data challenge

To unlock the value of machine learning models, organizations must train them on enterprise-specific proprietary data, enabling them to excel in specialized tasks. This is the most challenging task for machine learning teams. Referred to as the ‘data bottleneck’, the problem addresses the inability of organizations to rapidly extract value from AI due to challenges pertaining to training data availability, quality, or privacy. As a result, ML projects often fail to take flight, remain confined in innovation labs, and never reach production.

  • Data Quality

    Issues with data quality such as missing fields and unwanted bias greatly impact model performance, jeopardizing the utility of models in production.

  • Data Availability

    Training models requires large amounts of cleaned, curated, and annotated data. Collecting ground-truth data is time-consuming and expensive.

  • Data Privacy

    ML teams need access to sensitive data to train, evaluate, and improve models. Provisioning access to data takes months and raises compliance concerns.

Solution

The Gretel solution

Gretel empowers organizations to accelerate the last mile of ML training via safe access to synthetic data. Gretel's platform provides the end-to-end capabilities for generating, evaluating, and operationalizing synthetic data to improve ML generalizability and performance. This includes advanced anonymization of sensitive entities with mathematical privacy guarantees, augmentation of whole datasets, boosting limited classes, and even simulation of rare edge cases.

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Key Benefits

  • Improve ML performance

    Multiple synthetic data models purpose-built for producing high-quality and fully labeled data for more robust ML models.

  • Faster time to value

    Accelerate your most critical intelligent applications with on-demand access to training data that embeds directly in your ML pipelines.

  • Safe ML training

    Mathematically guaranteed privacy and mitigated risks of regulatory fines with provably private synthetic data.

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