Machine learning is an established and frequently used technique in industry and
academia, but a standard process model to improve success and efficiency of machine …

The last few years witnessed significant advances in building automated or semi-automated
data quality, data cleaning and data integration systems powered by machine learning (ML) …

Data cleaning is widely regarded as a critical piece of machine learning (ML) applications,
as data errors can corrupt models in ways that cause the application to operate incorrectly …

Machine learning (ML) models can fail in unexpected ways in the real world, but not all
model failures are equal. With finite time and resources, ML practitioners are forced to …

As automated machine learning (AutoML) systems continue to progress in both
sophistication and performance, it becomes important to understand the 'how'and 'why'of …

In the exploratory data science lifecycle, data scientists often spent the majority of their time
finding, integrating, validating and cleaning relevant datasets. Despite recent work on data …

Data quality validation is a crucial step in modern data-driven applications. Errors in the data
lead to unexpected behavior of production pipelines and downstream services, such as …

Data corruption is an impediment to modern machine learning deployments. Corrupted data
can severely bias the learned model and can also lead to invalid inferences. We present …

Data lakes have emerged as a solution for managing vast and diverse datasets for modern
data analytics. To prevent them from becoming ungoverned, semantic data management …

Complex data pipelines are increasingly common in diverse applications such as BI
reporting and ML modeling. These pipelines often recur regularly (eg, daily or weekly), as BI …