Website: Data Readiness Workshop
In the past decade, AI/ML technologies have become pervasive in academia and industry, finding their utility in newer and challenging applications. While there has been a focus to build better, smarter and automated ML models little work has been done to systematically understand the challenges in the data and assess its quality issues before it is fed to an ML pipeline. Issues such as incorrect labels, synonymous categories in a categorical variable, heterogeneity in columns etc. which might go undetected by standard pre-processing modules in these frameworks can lead to sub-optimal model performance. Although, some systems are able to generate comprehensive reports with details of the ML pipeline, a lack of insight and explainability w.r.t. to the data quality issues leads to data scientists spending ~80% time on data preparation before employing these AutoML solutions. This is why data preparation has been called out as one of the most time-consuming step in an AI lifecycle.
The goal of this workshop is to attract researchers working in the fields of data acquisition, data labeling, data quality, data preparation and AutoML areas to understand how the data issues, their detection and remediation will help towards building better models. With a focus on different modalities such as structured data, time series data, text data and graph data, this workshop invites researchers from academia and industry to submit novel propositions for systematically identifying and mitigating data issues for making data AI ready.
