Accelerate AI through improved 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-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.

Why adopt Data-Centric AI?

Benefit from data-centric AI flows

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