MOD

Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that simplifies how data is used by ML tools and frameworks. Croissant makes ...

Data scientists develop ML pipelines in an iterative manner: they repeatedly screen a pipeline for potential issues, debug it, and then revise and improve its code according to their findings. However, this manual process is tedious and error-prone. ...

Experts outside the field of machine learning (ML) are interested in using ML techniques to analyze their textual data, but they are inhibited by a lack of convenient natural language processing (NLP) tools. To address this issue, we present tailwiz, an ...

Analysts and scientists are interested in automatically analyzing the semantic contents of unstructured, non-tabular data (videos, images, text, and audio). These analysts have turned to unstructured data systems leveraging machine learning (ML). The ...

Satellites have become more widely available due to the reduction in size and cost of their components. As a result, there has been an advent of smaller organizations having the ability to deploy satellites with a variety of data-intensive applications ...

With the emergence of AI regulations, such as the EU AI Act, requirements for simple data lineage, enforcement of low data bias, and energy efficiency have become a priority for everyone offering AI services. Being pre-trained on versatile and a vast ...

Modern data analytics pipelines comprise traditional data transformation operations and pre-trained ML models deployed as user-defined functions (UDFs). Such pipelines, which we call ML workflows, generally produce erroneous results due to data errors ...

While LLM-based data wrangling approaches that process each row of data have shown promising benchmark results, computational costs still limit their suitability for real-world use cases on large datasets. We revisit code generation using LLMs for ...

As the Lakehouse architecture becomes more widespread, ensuring the reproducibility of data workloads over data lakes emerges as a crucial concern for data practitioners. However, achieving reproducibility remains challenging. The size of data pipelines ...

Recent research has shown the importance of tuning DBMS configuration knobs to achieve high performance. As a result, a large number of search-based and learning-based auto-tuning methods have been proposed. However, despite the promising results, we ...

The Deep Learning (DL) workflow involves several steps of data transformation. Evaluating various configurations at each step of the workflow may be a complex task when it comes to selecting DL models. This decision-making process requires basing ...