-
Data-Centric AI Why You Need a Scalable Data Labeling ProcessLearn how building a scalable data labeling process ensures that your ML models have enough accurately-labeled training data to be effective and efficient.
HumanSignal Team -
Data-Centric AI Report: Data science teams shift from model development to dataset developmentAs we wrap 2022, the Label Studio community survey reveals trends, investments and technology choices for data science teams in the year ahead.
Lauren SellVP of Marketing & Community
-
Data-Centric AI Guide: Building a Data Labeling Practice for Machine Learning and Data ScienceLearn the four core pillars of data labeling — data, process, people, and technology — and how to build a successful data labeling practice.
HumanSignal Team -
Data-Centric AI The Future Is Intelligent Data LabelingLearn why going from manual data labeling to intelligent data labeling could be the key to saving time and cost.
Nate KartchnerDirector of Marketing
-
Data-Centric AI Why Internal Data Labeling is the Right Choice for Data-Centric AIBetter ML/AI performance starts with accurate and consistent data, labeled by domain experts, accelerated by active learning.
Mark BrandstetterEnterprise Account Executive
-
Data-Centric AI Data-Centric AI: What is it, and why does it matter?Data-centric AI is a rapidly growing, data-first approach to building AI systems using high-quality data from the start and continually enhancing the dataset to improve the model's performance. Data-centric AI is a modern approach to building AI where model accuracy is primarily dependent on data quality.
Michael MalyukCEO & Co-Founder