YData SDK allows to use the *Data-Centric* tools from the YData ecosystem to accelerate AI development
Project description
YData SDK
🎊 YData SDK for improved data quality everywhere!
ydata-sdk v0.1.0 is here! Create a YData account so you can start using today!
Overview
The YData SDK is an ecosystem of methods that allows users to, through a python interface, adopt a Data-Centric approach towards the AI development. The solution includes a set of integrated components for data ingestion, standardized data quality evaluation and data improvement, such as synthetic data generation, allowing an iterative improvement of the datasets used in high-impact business applications.
Synthetic data can be used as Machine Learning performance enhancer, to augment or mitigate the presence of bias in real data. Furthermore, it can be used as a Privacy Enhancing Technology, to enable data-sharing initiatives or even to fuel testing environments.
Under the YData SDK hood, you can find a set of algorithms and metrics based on statistics and deep learning based techniques, that will help you to accelerate your data preparation.
What you can expect:
YData SDK is composed by the following main modules:
-
Datasources
- YData’s SDK includes several connectors for easy integration with existing data sources. It supports several storage types, like filesystems and RDBMS. Check the list of connectors.
- SDK’s Datasources run on top of Dask, which allows it to deal with not only small workloads but also larger volumes of data.
-
Synthesizers
- Simplified interface to train a generative model and learn in a data-driven manner the behavior, the patterns and original data distribution. Optimize your model for privacy or utility use-cases.
- From a trained synthesizer, you can generate synthetic samples as needed and parametrise the number of records needed.
-
Synthetic data quality report Coming soon
- An extensive synthetic data quality report that measures 3 dimensions: privacy, utility and fidelity of the generated data. The report can be downloaded in PDF format for ease of sharing and compliance purposes or as a JSON to enable the integration in data flows.
-
Profiling Coming soon
- A set of metrics and algorithms summarizes datasets quality in three main dimensions: warnings, univariate analysis and a multivariate perspective.
Supported data formats
- Tabular The RegularSynthesizer is perfect to synthesize high-dimensional data, that is time-independent with high quality results.
- Time-Series The TimeSeriesSynthesizer is perfect to synthesize both regularly and not evenly spaced time-series, from smart-sensors to stock.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
Hashes for ydata_sdk-0.12.1-py310-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 019a825e81d4dab1611cc52b853297a599e3e153353c26644e6e5134cb21d919 |
|
MD5 | 696f9e97aca0d99fbb00956547c98992 |
|
BLAKE2b-256 | 6401deb5b4e6843e845b9a3799abc1252832074170868c1361a238748f6b670d |
Hashes for ydata_sdk-0.12.1-py39-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3c97ff43277d16692568eefc26cb5bb9394e3216a449358d522870e4cf93b6ab |
|
MD5 | e0250d236051f048fd720f0f836c6fca |
|
BLAKE2b-256 | ee86f59ce3d856ba1bd908872422ad203785f1a57cca97bd4f28658fff2be12f |
Hashes for ydata_sdk-0.12.1-py38-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9fd6892a62838d5eef36bb9292339bd42441888a501b12b297979281c6b8455e |
|
MD5 | 70090cd7b436c3a4d7670a9e26dac8dc |
|
BLAKE2b-256 | e020c376d0f768033a361eda8d7dd8da039d97de5563b916283f1a35e2a2fcc2 |