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.

data_vs_model
data_centric_ai_ill_dark_background

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
fabric_home

Join the Data-Centric AI movement!

Become a Data-Centric AI expert with our community! 

Our Most Recent Articles

June 1, 2023
Blog

The Top 5 Python Packages to Generate Realistic Synthetic Data: 2023 Edition

Get started with Synthetic Data Generation with these Open Source Libraries Following the extraordinary advances of Generative AI models, Synthetic Data is becoming the standard for Machine Learning development. Especially with the rise of...

Read More
June 1, 2023
Blog

Understanding the Structure of Time-Series Datasets

Photo by Agê Barros on Unsplash Unveiling the inner workings of how sequential data works and how Fabric can to smooth your journey in a time-series Machine Learning project   Time-series data refers to a type of data that is collected and...

Read More
May 24, 2023
Blog

Synthetic data generation with Gaussian Mixture Models

Photo by Roman Synkevych on Unsplash   A probabilistic approach to fast synthetic data generation with ydata-synthetic To find synthetic data generation within the same sentence as Gaussian Mixture Models (GMMs) sounds odd, but it makes a...

Read More