Welcome tothe Data-Centric AI Community

Are you intrigued by the possibilities of Data-Centric AI, and you want more?

Join like-minded experts, thought-leaders and peers at the Data-Centric Community!

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Want to understand Data-Centric AI better?

Data-Centric AI is the process of building and testing AI systems by focusing on data-centric operations (i.e. cleaning, cleansing, pre-processing, balancing, augmentation) rather than model-centric operations (i.e. hyper-parameters selection, architectural changes).

The term is coined by Andrew Ng. The definition is provided by Marco Postiglione and chosen by the community itself.

The Data-Centric AI Community is the place to discuss data quality for data science.


Data Profiling

Understanding the existing data is the first step. Profile your data in a few lines of code.

Explore your data with ydata-profiling!0

Synthetic Data

Synthetic data is artificially created data that keeps the original data properties, ensuring its business value while being privacy compliant.

Expand your data with ydata-synthetic!0

Data Labeling

Isn’t it one of your biggest pain points in data quality? The DCAI Community cultivates meaningful discussions around this and other topics!

Coming soon

Want to learn more?


Introducing the Data-Centric AI Community

A place to discuss data quality for data science.

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From model-centric to data-centric

A new paradigm for AI development — focused on data quality.

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Pandas Profiling for Quicker Data Understanding

Read your data? Pause. Generate the Pandas Profiling report first.

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Synthetic Time-Series Data: A GAN approach

Generate synthetic sequential data with TimeGAN.

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How to Validate the Quality of Your Synthetic Data

A tutorial on how you can combine ydata-synthetic with great expectations.

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Versioning and Labeling — Better Together

Data labeling and data versioning provide a rock solid bedrock to build your machine learning models on now and in the future.

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