OCTOBER 19TH - 21ST: TransformX 2022 - Operationalizing AI and Machine Learning by Scale

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Synthetic

Boost ML performance with diverse and realistic synthetic data.

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Why Synthetic

Enhancing model performance by complementing real-world datasets

Scale Synthetic helps teams overcome the inability to collect enough edge cases, data
collection bias, and data privacy issues with augmented or fully synthetic data.

Augmenting Data
Augment ground-truth training data with infinite varieties of synthetic data and seamlessly integrate with existing ML data pipelines.
Boost Model Performance
Create synthetic datasets from scratch, or combine synthetic and real data to enhance performance on key edge cases.
Close the Reality Gap
Leverage Scale's unique human-in-the-loop synthetic data generation process to create realistic synthetic data.
Privacy and Bias Avoidance
Overcome data privacy and bias issues with synthetic data generated according to specified parameter distributions.
Leverage Your Data
Seamlessly integrate your synthetic data with Scale’s ecosystem of data-centric ML products to get the most out of your data.
Rapid Prototyping
Reduce data collection, curation, and annotation wait times to enable faster experimentation and prototyping.

DATA INPUTS

Supported Data Types

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How It Works

Build Better Datasets

Scale Synthetic is designed to support the augmentation of real data or to generate fully synthetic data from scratch. Achieve your desired data distribution to improve model performance.

“Kodiak uses synthetic data not as an alternative to real-world data, but as a complement; Scale Synthetic is an important enabler of that approach. By using Scale Synthetic to efficiently generate a large number of rare edge cases, the Kodiak Driver leverages the best of both real and synthetic data. Everything integrates seamlessly with our existing data labeling pipeline and data”