Data-Centric AI

Accelerate AI through improved data

Let data be the focus of AI development. Better data, improved Machine Learning performance.

“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)”

- Data-Centric AI Community

What is data-centric AI?

Focus on your data

The centerpiece of Machine Learning has always been around data - "Garbage in, garbage out" had been widely used when talking about Analytics and Machine Learning, but only more recently, and with the advent of considerable and sophisticated models, data science teams have decided to shift their focus to data. Data-Centric AI is the process of iterating, collaborating, and optimizing the quality of the data to enhance the performance of models. 

 

Model-Centric vs Data-Centric

Under the Model-Centric AI umbrella and in the Machine Learning equation, the data is the fixed variable. 

The mindset that data is a fixed artifact leads to its exclusion from the models' development process. In a reality where real data is noisy, focusing on algorithms and architectures, parameters selection, and data architectures is not enough for AI success.

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Data-Centric AI is a pragmatic approach to developing Machine Learning and Data Science solutions that makes sense when working with real-world data. Data is now part of the Machine Learning iterative development process, and its stakes are higher regarding business value delivered by AI.

Becoming "data-centric" means - spending more time managing, profiling, augmenting, and curating data efficiently in a reproducible manner. 

 

Why adopt Data-Centric AI?

Benefit from data-centric AI flows

Improved AI performance

A Data-Centric AI approach translates into high-quality data. With better data, the developed solutions are more resilient hence return improved performance for businesses and organizations.

Faster development & time-to-market

Simplified, scalable and simple connection to a variety of data sources. Understand your data assets through automated profiling and detection of quality issues for faster exploratory data analysis and data 

Collaborative & Efficient

Simplified, scalable and simple connection to a variety of data sources. Understand your data assets through automated profiling and detection of quality issues for faster exploratory data analysis and data 

Fabric

Applied Data-Centric AI

YData Fabric accelerates and increases machine learning and data science teams productivity.
The Data-Centric AI workbench that let you understand the sources of noise and bias in your datasets, improve the accuracy and boost the performance of your models
Upload your data from FileSystems to RDBMS' 
Understand your data assets with automated data profiling
Improve data quality with synthetic data
Experiment in a familiar environment with Jupyter Labs and VS Code
Build, version & orchestrate your data preparation flows with pipelines
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Join the Data-Centric AI movement!

Become a Data-Centric AI expert with our community! 

Our Most Recent Articles

September 27, 2023
Blog

Data-Centric AI in Business: Strategies for Leveraging Data as an Asset

In the last decade, we’ve increasingly focused on model-centric Artificial Intelligence, building ever more flexible machine learning models. However, a new paradigm shift – Data-Centric AI – is currently revolutionizing the industry, as...

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Research

YData Fabric Synthetic data vs SDV

Synthetic data is a cornerstone of Data Centric-AI, an approach that focuses primarily on data quality rather than models. For the past few years, synthetic data gained attention because of a wide range of applications such as data...

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Blog

Accelerating AI Development with Synthetic Data: Strategies for Effective Implementation

In the rapidly evolving Artificial Intelligence landscape, data quality is the lifeblood that fuels the development of accurate and efficient models. However, accessing and acquiring high-quality, diverse, and labeled data can be quite a...

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