Use case

Improve Machine Learning Performance with Synthetic Data

Boost machine learning (ML) training datasets with Gretel's synthetic data platform to improve model performance and ensure data privacy and security across the MLOps lifecycle.
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Challenge

The ML training challenge

To unlock the value of machine learning models, organizations must train them on domain-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, data quality, or data 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 ML training data solution

Gretel empowers organizations to accelerate the last mile of ML training via safe access to synthetic data. Gretel's synthetic data platform provides the end-to-end capabilities for generating, evaluating, and operationalizing synthetic data to improve ML robustness 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 machine learning 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 synthetic training data that embeds directly in your ML pipelines.

  • Safe training data for machine learning

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

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