The Pipeline for the Continuous Development of Artificial Intelligence
Models -- Current State of Research and Practice
- URL: http://arxiv.org/abs/2301.09001v1
- Date: Sat, 21 Jan 2023 20:04:07 GMT
- Title: The Pipeline for the Continuous Development of Artificial Intelligence
Models -- Current State of Research and Practice
- Authors: Monika Steidl, Michael Felderer, Rudolf Ramler
- Abstract summary: This paper includes a Multivocal Literature Review where we consolidated 151 relevant formal and informal sources.
We map challenges regarding pipeline implementation, adaption, and usage for the continuous development of AI to these four stages.
- Score: 3.793596705511303
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Companies struggle to continuously develop and deploy AI models to complex
production systems due to AI characteristics while assuring quality. To ease
the development process, continuous pipelines for AI have become an active
research area where consolidated and in-depth analysis regarding the
terminology, triggers, tasks, and challenges is required. This paper includes a
Multivocal Literature Review where we consolidated 151 relevant formal and
informal sources. In addition, nine-semi structured interviews with
participants from academia and industry verified and extended the obtained
information. Based on these sources, this paper provides and compares
terminologies for DevOps and CI/CD for AI, MLOps, (end-to-end) lifecycle
management, and CD4ML. Furthermore, the paper provides an aggregated list of
potential triggers for reiterating the pipeline, such as alert systems or
schedules. In addition, this work uses a taxonomy creation strategy to present
a consolidated pipeline comprising tasks regarding the continuous development
of AI. This pipeline consists of four stages: Data Handling, Model Learning,
Software Development and System Operations. Moreover, we map challenges
regarding pipeline implementation, adaption, and usage for the continuous
development of AI to these four stages.
Related papers
- MQG4AI Towards Responsible High-risk AI - Illustrated for Transparency Focusing on Explainability Techniques [1.1105279729898387]
We propose an approach for AI lifecycle planning that bridges the gap between generic guidelines and use case-specific requirements.
Our work aims to contribute to the development of practical tools for implementing Responsible AI (RAI)
arXiv Detail & Related papers (2025-02-17T15:14:52Z) - Generative AI Application for Building Industry [10.154329382433213]
This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs) in the building industry.
The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices.
arXiv Detail & Related papers (2024-10-01T21:59:08Z) - 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) - CI-Bench: Benchmarking Contextual Integrity of AI Assistants on Synthetic Data [7.357348564300953]
CI-Bench is a comprehensive benchmark for evaluating the ability of AI assistants to protect personal information during model inference.
We present a novel, scalable, multi-step data pipeline for generating natural communications, including dialogues and emails.
We formulate and evaluate a naive AI assistant to demonstrate the need for further study and careful training towards personal assistant tasks.
arXiv Detail & Related papers (2024-09-20T21:14:36Z) - The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources [100.23208165760114]
Foundation model development attracts a rapidly expanding body of contributors, scientists, and applications.
To help shape responsible development practices, we introduce the Foundation Model Development Cheatsheet.
arXiv Detail & Related papers (2024-06-24T15:55:49Z) - Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation [1.6230958216521798]
This study investigates the potential of leveraging the pre-trained Large Language Models (LLMs)
By adopting ChatGPT API as the reasoning core, we outline an integrated workflow that encompasses natural language processing, methontology-based prompt tuning, and transformers.
The outcomes of our methodology are knowledge graphs in widely adopted ontology languages (e.g., OWL, RDF, SPARQL)
arXiv Detail & Related papers (2024-05-29T16:40:31Z) - Dealing with Data for RE: Mitigating Challenges while using NLP and
Generative AI [2.9189409618561966]
Book chapter explores the evolving landscape of Software Engineering in general, and Requirements Engineering (RE) in particular.
We discuss challenges that arise while integrating Natural Language Processing (NLP) and generative AI into enterprise-critical software systems.
Book provides practical insights, solutions, and examples to equip readers with the knowledge and tools necessary.
arXiv Detail & Related papers (2024-02-26T19:19:47Z) - TAT-LLM: A Specialized Language Model for Discrete Reasoning over Tabular and Textual Data [73.29220562541204]
We consider harnessing the amazing power of language models (LLMs) to solve our task.
We develop a TAT-LLM language model by fine-tuning LLaMA 2 with the training data generated automatically from existing expert-annotated datasets.
arXiv Detail & Related papers (2024-01-24T04:28:50Z) - Generative AI [20.57872238271025]
"generative AI" refers to computational techniques that are capable of generating seemingly new, meaningful content.
The widespread diffusion of this technology with examples such as Dall-E 2, GPT-4, and Copilot is currently revolutionizing the way we work and communicate with each other.
arXiv Detail & Related papers (2023-09-13T08:21:59Z) - Demonstrate-Search-Predict: Composing retrieval and language models for
knowledge-intensive NLP [77.817293104436]
We propose a framework that relies on passing natural language texts in sophisticated pipelines between an LM and an RM.
We have written novel DSP programs for answering questions in open-domain, multi-hop, and conversational settings.
arXiv Detail & Related papers (2022-12-28T18:52:44Z) - A Research Agenda for Artificial Intelligence in the Field of Flexible
Production Systems [53.47496941841855]
Production companies face problems when it comes to quickly adapting their production control to fluctuating demands or changing requirements.
Control approaches aiming to encapsulate production functions in the sense of services have shown to be promising in order to increase flexibility of Cyber-Physical Production Systems.
But an existing challenge of such approaches is finding production plans based on provided functionalities for a set of requirements, especially when there is no direct (i.e., syntactic) match between demanded and provided functions.
arXiv Detail & Related papers (2021-12-31T14:38:31Z) - SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines.
This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
arXiv Detail & Related papers (2021-12-22T14:45:37Z)
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.