MOSTLY AI

Overview

MOSTLY AI provides a synthetic data generation platform that enables organizations to create artificial datasets from real-world, sensitive information. The platform is engineered to generate synthetic data that maintains the statistical properties, patterns, and relationships of the original data, crucial for downstream analytical tasks and machine learning model training, while replacing individual records with entirely new, non-identifiable data points. This approach addresses privacy concerns associated with using or sharing production data, particularly in regulated industries.

The core application of MOSTLY AI's technology is to facilitate data-driven development and innovation in environments where access to authentic customer data is restricted due to privacy regulations such as GDPR or internal compliance policies. By producing synthetic versions, developers and data scientists can access realistic, high-fidelity datasets for training machine learning models, testing software applications, developing new features, and conducting analytics without compromising individual privacy. This can accelerate development cycles and reduce bottlenecks traditionally caused by data access limitations.

The platform is designed for technical users, including data scientists, machine learning engineers, and developers, who require programmatic control over data generation and integration into existing data pipelines. It offers a Python SDK and a REST API, allowing for automation and customization of synthetic data creation workflows. Use cases extend across various sectors, from financial services and healthcare, where data privacy is paramount, to retail and telecommunications, where large volumes of customer data are processed. According to a McKinsey report, synthetic data is gaining traction as a method to unlock insights from sensitive data while mitigating privacy risks.

One of the primary benefits of synthetic data is its utility in testing and quality assurance. Development teams can use synthetic data to create comprehensive test suites that cover various edge cases and scenarios, ensuring software robustness before deployment. This is particularly valuable in scenarios involving personal identifiable information (PII) or protected health information (PHI), where using real data for testing is often prohibited. Furthermore, MOSTLY AI's solution supports secure data sharing within an organization or with external partners, enabling collaboration without direct exposure of sensitive information, thereby enhancing data governance and reducing the risk of data breaches.

Key features

• High-Fidelity Synthetic Data Generation: Creates statistically representative synthetic datasets that preserve the patterns, distributions, and relationships present in the original data, suitable for analytics and AI model training.
• Privacy Preservation: Generates entirely new data points, ensuring no direct link to individual records in the source data, addressing GDPR and other privacy compliance requirements.
• Automated Data Anomaly Detection: Includes capabilities to identify and handle anomalies in source data during the synthesis process, improving the quality of the generated output.
• Python SDK and REST API: Provides programmatic interfaces for integrating synthetic data generation into existing data pipelines and automating workflows (MOSTLY AI API Reference).
• Data Utility Evaluation: Offers tools and metrics to assess the statistical resemblance and utility of the synthetic data compared to the original, helping users validate its fitness for purpose.
• Data Masking and Transformation: Supports various data masking techniques and transformations to prepare sensitive data for synthesis or to enhance privacy further.
• Scalability: Designed to handle large volumes of data, enabling the synthesis of extensive datasets required for enterprise-level applications and big data environments.
• Free Community Edition: A no-cost version available for individual use and evaluation, providing access to core synthetic data capabilities.

Pricing

MOSTLY AI offers a free Community Edition, with paid tiers structured based on usage and feature requirements. The Starter tier is available with a monthly subscription, while higher-volume and enterprise-grade features are provided through custom pricing models.

For detailed pricing information and current offerings, refer to the MOSTLY AI pricing page.

Common integrations

• Data Warehouses/Lakes: Integration with platforms like Snowflake, Databricks, and AWS S3 for ingesting source data and exporting synthetic data.
• Machine Learning Platforms: Compatibility with ML platforms and frameworks for training models using synthetic datasets.
• BI Tools: Connection with business intelligence tools for analytical workflows using privacy-preserving synthetic data.
• Development Environments: SDK and API integration with developer tools and CI/CD pipelines for automated data provisioning.

Alternatives

• Gretel.ai: Offers APIs for synthetic data generation and anonymization, focusing on developer-friendly tools.
• Syntho: Provides a synthetic data platform emphasizing data utility and privacy for enterprise use cases.
• Hazy: Specializes in enterprise synthetic data generation for privacy-preserving data sharing and development.

Getting started

To begin using MOSTLY AI for synthetic data generation, you can utilize their Python SDK. The following example demonstrates how to connect to the platform, load a dataset, and generate synthetic data.

import pandas as pd
from mostlyai import MostlyAI

# Replace with your API key and instance URL
mostly_ai = MostlyAI(api_key="YOUR_API_KEY", instance_url="YOUR_INSTANCE_URL")

# Load your original dataset (example using a DataFrame)
data = {
    'customer_id': [1, 2, 3, 4, 5],
    'age': [30, 24, 45, 33, 28],
    'salary': [50000, 45000, 70000, 55000, 48000],
    'city': ['New York', 'London', 'Paris', 'New York', 'Berlin']
}
df_original = pd.DataFrame(data)

# Upload the original dataset to MOSTLY AI
dataset = mostly_ai.datasets.upload_dataframe(df_original, name="MyOriginalDataset")
print(f"Original dataset uploaded: {dataset.name} (ID: {dataset.id})")

# Start a synthetic data generation job
synthesis_job = dataset.synthesize()
print(f"Synthesis job started: {synthesis_job.id}")

# Wait for the job to complete (optional, you can also check status asynchronously)
synthesis_job.wait_for_completion()

# Download the synthetic dataset
df_synthetic = synthesis_job.download_dataframe()
print("Synthetic data generated successfully:")
print(df_synthetic.head())

# You can now use df_synthetic for your development, testing, or analytics tasks

This Python code snippet illustrates the basic workflow: authenticating with the platform, uploading source data, initiating a synthesis job, and downloading the resulting synthetic dataset. For more comprehensive examples and detailed API documentation, refer to the MOSTLY AI Python SDK Guide.