Data-Centric AI
The tools for training sophisticated AI models have become so powerful that what limits them the most is the quality of the data used to train them.
Data-Centric AI is a revolutionary change in how you build AI from tinkering with models to curating better, more accurate and fairer data.
Data-Centric AI is a revolutionary change in how you build AI from tinkering with models to curating better, more accurate and fairer data.
“The goal of data-centric AI is to shift the focus of AI development from fine tuning models to curating better data. Sophisticated models 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 and co-founder of Modulos
“If the previous decade of ML/AI system research was driven by scalability, efficiency, and automation, we believe among the defining challenges of ML/AI systems for the coming decade is the need to manage, facilitate, and enforce trustworthiness.”
Ce Zhang / Modulos co-founder and professor at ETH Zurich
Why should you adopt
Data-Centric AI?
Using data-centric AI approaches will unlock the potential of your data for building AI/ML applications.
- You will be able to reduce your effort and expense in curating and labeling data
- You will be able to build models whose performance is better
- You will be ready to master new compliance requirements needed to bring AI services to market
"Modulos enabled our subject matter experts to quickly explore the possibilities of machine learning."
"At OCAS, Modulos provided considerable added value to our own digital efforts. In just two months, we analyzed several complex datasets and built well-performing ML models."
"We have been using Modulos AutoML in our research group and it is working extremely well, making it much easier for anyone to carry out ML projects, even without a strong CS background."
- 10Users
- 5Projects
"I was working on a deep learning project in astrophysics when I first tried out Modulos AutoML, and it would have saved me several months of work that I spent on model selection!"
Go with Modulos to give your data science team an edge
