AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component of contemporary coding contexts. Among these environments, computational notebooks …
The introduction of machine learning (ML) components in software projects has created the need for software engineers to collaborate with data scientists and other specialists. While …
Computational notebooks-such as Azure, Databricks, and Jupyter-are a popular, interactive paradigm for data scientists to author code, analyze data, and interleave visualizations, all …
Effective collaboration in data science can leverage domain expertise from each team member and thus improve the quality and efficiency of the work. Computational notebooks …
Interfaces for machine learning (ML), information and visualizations about models or data, can help practitioners build robust and responsible ML systems. Despite their benefits …
Organizations rely on machine learning engineers (MLEs) to operationalize ML, ie, deploy and maintain ML pipelines in production. The process of operationalizing ML, or MLOps …
Data scientists often have to use other presentation tools (eg, Microsoft PowerPoint) to create slides to communicate their analysis obtained using computational notebooks. Much …
Data analysis is challenging as analysts must navigate nuanced decisions that may yield divergent conclusions. AI assistants have the potential to support analysts in planning their …
Data scientists have embraced computational notebooks to author analysis code and accompanying visualizations within a single document. Currently, although these media …