Data-centric AI software company Modulos AG today announced the availability of its revolutionary data-centric AI platform. The platform enables companies to identify flaws in their data in a fraction of the time required by conventional data cleaning methods. These practical recommendations then help users build better AI/ML models based on the improved data.
Recent studies of how data scientists spend their time regularly highlight that curating data and then manually inspecting and cleaning it can take up to 80% of their time. (Ref: hbr.org) These efforts by highly trained specialists lengthen the time and increase the cost of AI/ML projects. Even with all this human effort spent on improving data quality, only 13% of AI/ML applications make it into production.
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The Modulos platform recommendations can reduce the time spent on data cleaning and quality checks by pinpointing exactly which data samples most affect the performance of AI models trained with them.
“The goal of data-centric AI is to shift the focus of AI development from fine-tuning models to curating better data. AI trained on flawed data can’t result in accurate and trustworthy models. That’s why most of the human effort in building AI systems should focus on data quality.”
– Kevin Schawinski, CEO of Modulos
The European Union is currently working on an EU AI Act which will set the global standard for how AI products and services must be developed and brought to market. Amongst the key requirements of this Act is that the data used to train AI is high quality, complete and fair.
Modulos gives users the tools to develop better and more fair AI applications in line with the coming regulatory requirements.
What is data-centric AI, and why is it so revolutionary?
This short video explains it all.
Data-centric AI – The difference between winners and losers
AI is a changing business, and data-centric AI will play a major role in its evolution. To emphasise the importance of AI, note what McKinsey said on this matter.
McKinsey estimates that AI will add $13 trillion to the global economy over the next decade, yet companies are still struggling to scale up their AI efforts. The difference between the winners and losers in this transformation will be determined not by whether you have implemented AI, but by how you have, and who you have involved in the process