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A meta-summary of challenges in building products with ml components–collecting experiences from 4758+ practitioners
Incorporating machine learning (ML) components into software products raises new
software-engineering challenges and exacerbates existing ones. Many researchers have …
software-engineering challenges and exacerbates existing ones. Many researchers have …
On the design of ai-powered code assistants for notebooks
AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component
of contemporary coding contexts. Among these environments, computational notebooks …
of contemporary coding contexts. Among these environments, computational notebooks …
Collaboration challenges in building ml-enabled systems: Communication, documentation, engineering, and process
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 …
need for software engineers to collaborate with data scientists and other specialists. While …
Operationalizing machine learning: An interview study
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 …
and maintain ML pipelines in production. The process of operationalizing ML, or MLOps …
What's wrong with computational notebooks? Pain points, needs, and design opportunities
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 …
paradigm for data scientists to author code, analyze data, and interleave visualizations, all …
How do data analysts respond to ai assistance? a wizard-of-oz study
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 …
divergent conclusions. AI assistants have the potential to support analysts in planning their …
Symphony: Composing interactive interfaces for machine learning
Interfaces for machine learning (ML), information and visualizations about models or data,
can help practitioners build robust and responsible ML systems. Despite their benefits …
can help practitioners build robust and responsible ML systems. Despite their benefits …
How data scientists use computational notebooks for real-time collaboration
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 …
member and thus improve the quality and efficiency of the work. Computational notebooks …
Documentation matters: Human-centered ai system to assist data science code documentation in computational notebooks
Computational notebooks allow data scientists to express their ideas through a combination
of code and documentation. However, data scientists often pay attention only to the code …
of code and documentation. However, data scientists often pay attention only to the code …
How do analysts understand and verify ai-assisted data analyses?
Data analysis is challenging as it requires synthesizing domain knowledge, statistical
expertise, and programming skills. Assistants powered by large language models (LLMs) …
expertise, and programming skills. Assistants powered by large language models (LLMs) …