Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists

• URL: http://arxiv.org/abs/2103.10548v1
• Date: Thu, 18 Mar 2021 22:25:44 GMT
• Title: Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists
• Authors: Filippo Lanubile, Fabio Calefato, Luigi Quaranta, Maddalena Amoruso, Fabio Fumarola, Michele Filannino
• Abstract summary: The transition from AI/ML models to production-ready AI-based systems is a challenge for both data scientists and software engineers. In this paper, we report the results of a workshop conducted in a consulting company to understand how this transition is perceived by practitioners.
• Score: 10.27276267081559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transition from AI/ML models to production-ready AI-based systems is a challenge for both data scientists and software engineers. In this paper, we report the results of a workshop conducted in a consulting company to understand how this transition is perceived by practitioners. Starting from the need for making AI experiments reproducible, the main themes that emerged are related to the use of the Jupyter Notebook as the primary prototyping tool, and the lack of support for software engineering best practices as well as data science specific functionalities.

Related papers

• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting [36.31269406067809]
  We argue that data-centric AI is essential for training AI models, particularly for transformer-based TSF models efficiently. We review the previous research works from a data-centric AI perspective and we intend to lay the foundation work for the future development of transformer-based architecture and data-centric AI.
  arXiv  Detail & Related papers  (2024-07-29T08:27:21Z)
• What About the Data? A Mapping Study on Data Engineering for AI Systems [0.0]
  There is a growing need for data engineers that know how to prepare data for AI systems. We found 25 relevant papers between January 2019 and June 2023, explaining AI data engineering activities. This paper creates an overview of the body of knowledge on data engineering for AI.
  arXiv  Detail & Related papers  (2024-02-07T16:31:58Z)
• Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis [82.59451639072073]
  General-purpose robots operate seamlessly in any environment, with any object, and utilize various skills to complete diverse tasks. As a community, we have been constraining most robotic systems by designing them for specific tasks, training them on specific datasets, and deploying them within specific environments. Motivated by the impressive open-set performance and content generation capabilities of web-scale, large-capacity pre-trained models, we devote this survey to exploring how foundation models can be applied to general-purpose robotics.
  arXiv  Detail & Related papers  (2023-12-14T10:02:55Z)
• AI in Software Engineering: A Survey on Project Management Applications [3.156791351998142]
  Machine Learning (ML) employs algorithms that undergo training on data sets, enabling them to carry out specific tasks autonomously. AI holds immense potential in the field of software engineering, particularly in project management and planning.
  arXiv  Detail & Related papers  (2023-07-27T23:02:24Z)
• AutoML-GPT: Automatic Machine Learning with GPT [74.30699827690596]
  We propose developing task-oriented prompts and automatically utilizing large language models (LLMs) to automate the training pipeline. We present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyper parameters. This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas.
  arXiv  Detail & Related papers  (2023-05-04T02:09:43Z)
• Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production [1.332560004325655]
  Data scientists must transition from the explorative phase of Machine Learning projects to their production phase. To narrow the gap between these two phases, tools and practices adopted by data scientists might be improved by incorporating consolidated software engineering solutions. In my research project, I study the best practices for collaboration with computational notebooks and propose proof-of-concept tools to foster guidelines compliance.
  arXiv  Detail & Related papers  (2022-05-24T10:13:38Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Machine Learning for Software Engineering: A Systematic Mapping [73.30245214374027]
  The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. No comprehensive study exists that explores the current state-of-the-art on the adoption of machine learning across software engineering life cycle stages. This study introduces a machine learning for software engineering (MLSE) taxonomy classifying the state-of-the-art machine learning techniques according to their applicability to various software engineering life cycle stages.
  arXiv  Detail & Related papers  (2020-05-27T11:56:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.