Democratizing artificial intelligence: How no-code AI can leverage machine learning operations

Sundberg, Leif; Holmström, Jonny

doi:10.1016/j.bushor.2023.04.003

Cited by 39 publications

(12 citation statements)

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“…The advent and evolution of multimodal large language models, exemplified by OpenAI's ChatGPT-4, offers a substantial opportunity to leverage the increasing amount of data being generated in the health care sector [13][14][15]. The need to democratize AI is becoming increasingly recognized [16], with an emphasis on "no-code AI" [17]. Models like ChatGPT can make complex biological data more accessible and understandable to a broader audience, enabling more collaboration among all stakeholders, not only researchers and clinical providers but also patients to better grasp the intricacies of health and disease.…”

Section: Discussionmentioning

confidence: 99%

ChatGPT and Medicine: Together We Embrace the AI Renaissance

Hacking

2024

JMIR Bioinform Biotech

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The generative artificial intelligence (AI) model ChatGPT holds transformative prospects in medicine. The development of such models has signaled the beginning of a new era where complex biological data can be made more accessible and interpretable. ChatGPT is a natural language processing tool that can process, interpret, and summarize vast data sets. It can serve as a digital assistant for physicians and researchers, aiding in integrating medical imaging data with other multiomics data and facilitating the understanding of complex biological systems. The physician’s and AI’s viewpoints emphasize the value of such AI models in medicine, providing tangible examples of how this could enhance patient care. The editorial also discusses the rise of generative AI, highlighting its substantial impact in democratizing AI applications for modern medicine. While AI may not supersede health care professionals, practitioners incorporating AI into their practices could potentially have a competitive edge.

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Section: Discussionmentioning

confidence: 99%

ChatGPT and Medicine: Together We Embrace the AI Renaissance

Hacking

2024

JMIR Bioinform Biotech

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“…The scalability of MLOps workflows will be enhanced. Therefore, these workflows can dynamically scale the resources, like computing, storage, and networking, to handle the inconsistent workload and data volume [73,76].…”

Section: Technical Challengesmentioning

confidence: 99%

“…Aiding ML infrastructure management corresponds to the number of training examples to guarantee that the models are efficient, scalable, and reliable. Through the allocation of sufficient infrastructure resources together with end-to-end data science and DevOps tools targeting ML engineers, data scientists, and DevOps specialists, organizations can simplify and automate the process of developing, testing, deploying, and monitoring models [73].…”

Section: Technical Challengesmentioning

confidence: 99%

“…Technical Capability Maturity Model [74], Automate the complete end-to-end machine learning lifecycle [74], manual data science-driven process [74], Seamless demand for ML Training [2], Testbased Verification [78], Continuous Training [79], Model Provenance [81], Simplify the orchestration of business services [20], Considerations must be given to interoperability and scalability [73,76], measured and tracked [74], standardized experimental-operational symmetry process [74], Long release cycles [77], Resume-driven-development [77], Bias Mitigation [79], Continuous integration [13,74,79], Integrate software development and ML workflow processes [74], process integration [76], Mixed team dynamics [78], Continuous testing [74,82], Aiding ML infrastructure management [73], Infrastructure provisioning [2], infrastructure [76],…”

Section: Categories Individual Factors Based On Primary Studiesmentioning

confidence: 99%

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MLOps critical success factors - A systematic literature review

Mehmood,

Sabahat,

Muhammad Arsal Ijaz

2024

VFAST trans. softw. eng.

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MLOps encompasses a collection of practices integrating machine learning into operational activities, a recent addition to the diverse array of machine learning process models. The need to tightly integrate machine learning with information systems operations to ensure organizational performance led to the development of this approach. Therefore, MLOps methodologies are useful for businesses that want to make their ML operations and procedures more efficient. The purpose of this study is to summarize the many critical success factors that have been identified in studies focusing on MLOps initiatives. The paper shows how these CSFs affect MLOps performance and what factors drive this influence. We picked primary papers for analysis after conducting searches in three major publishing databases. We narrowed the field down to 58 unique CSFs, which were then classified according to three dimensions: technical, organizational, social and cultural. These CSFs affect and drive performance in MLOps, based on the results of the literature review. Researchers and industrial experts may enhance their understanding of CSFs and get insights into tackling MLOps difficulties inside organizations. The paper, notably, emphasizes several prospective research directions linked to CSFs.

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“…The outputs of these analyses can be downloaded for later use. The ability to generate and run scripts using native spoken language can contribute to the so‐called democratization of coding (e.g., Sundberg & Holmström, 2023), allowing scripts and codes to be generated by those with potentially no experience.…”

Section: An Overview Of the Advanced Data Analysis Plugin And Ai Scri...mentioning

confidence: 99%

Opportunities and limitations of the ChatGPT Advanced Data Analysis plugin for hydrological analyses

Irvine,

Halloran,

Brunner

2023

Hydrological Processes

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Artificial intelligence (AI) tools, such as ChatGPT, have already changed the way that many industries operate. Much of the recent discussion surrounding AI tools has focused on their ability to generate text and the associated ethical concerns. Recent developments with the ChatGPT Code Interpreter, now called the Advanced Data Analysis plugin, demonstrate that AI's growing abilities in the generation, translation and adaptation of computer scripts also offer significant potential to change the way that data analyses are conducted. We demonstrate the new capabilities of ChatGPT, providing four worked examples of prompt‐based generation of Python scripts to analyse hydrological datasets. Our examples include (1) converting R functions to Python to perform baseflow separation, (2) converting MATLAB functions to Python to ‘web scrape’ temperature data, (3) creating a correlation matrix of major ion groundwater data, and (4) presenting boxplots of river flow data. We discuss the significant opportunities, current limitations and concerns relating to the use of ChatGPT in hydrological data analyses.

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Democratizing artificial intelligence: How no-code AI can leverage machine learning operations

Cited by 39 publications

References 50 publications

ChatGPT and Medicine: Together We Embrace the AI Renaissance

ChatGPT and Medicine: Together We Embrace the AI Renaissance

MLOps critical success factors - A systematic literature review

Opportunities and limitations of the ChatGPT Advanced Data Analysis plugin for hydrological analyses

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