How prepared is your business to make the most of AI?

Zamora, Javier Flores; Herrera, Pedro Luis

doi:10.15581/002.art-3222

“…We evaluated the resulting codebook and tested whether each code could replace the corresponding quotation without changing its meaning. It helped understand and refine the nomenclature of the concepts (codes) that differed but were inserted in a similar context (e.g., increase the computational capability or take advantage of the latest hardware [33], [51]).…”

Section: Discussionmentioning

confidence: 99%

“…The practices listed in the Problem Definition and Solution Design and Product Management categories are fundamental for model validation. We observed studies that demonstrate this concern and mention practices such as Establish what is the outcome and what the data can offer [51] and Define the Desired Outcome [71]. That help in this understanding and alignment of objectives and primarily impacts the management and testing of the dataset Create the dataset specification [72] and Ensure the Reliability, Availability, and Quality of Data [109] and the model after training Stabilish the model requirements [123] and Model Evaluation and result interpretation [121], [92].…”

Section: A Impact Of Problem Understandingmentioning

confidence: 96%

“…Source Practice 1 papers/posts Business understanding and validation [3], [2], [56], [57], [32], [58], [59], [60], [19], [61], [62] [52], [63], [64], [65], [66], [51], [18], [67], [68], [69] 2 papers/posts Identifying the right problem to solve [70], [32], [15] [71], [72], [73], [74], [75], [76], [77], [18], [67], [78], [68] 3 posts Verify how necessary is ML for the product [15] [79], [52], [80], [81], [82], [55], [64], [83], [84], [85], [86], [87], [66], [4], [51], [71], ...…”

Section: Idmentioning

confidence: 99%

See 1 more Smart Citation

Practices for Managing Machine Learning Products: a Multivocal Literature Review

alves¹,

Leite²,

Meirelles³

et al. 2023

Preprint

0

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<p>Machine Learning (ML) has grown in popularity in the software industry due to its ability to solve complex problems. Developing ML Systems involves more uncertainty and risk because it requires identifying a business opportunity and managing the source code, data, and trained model. Our research aims to identify the existing practices used in the industry for building ML applications. The goal is to comprehend the orga- nizational complexity of adopting ML Systems. We conducted a Multivocal Literature Review (MLR) and used Grounded Theory (GT) to build a taxonomy with the practices applied to the ML System lifecycle from the industry and academic perspectives. We selected 41 posts from grey literature and 37 papers from scientific repositories. Following a systematic GT protocol, we mapped 91 practices, grouped in 6 core categories related to designing, developing, testing, and deploying ML Systems. The results can help organizations identify the gaps in their current ML processes and practices, and provide a roadmap for improving and optimizing their ML systems. The comprehensive taxonomy of practices developed in this research serves as a valuable tool for managers, practitioners, and researchers in the ML field, providing a clear and organized understanding of the complexity of managing ML systems.</p>

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“…We evaluated the resulting codebook and tested whether each code could replace the corresponding quotation without changing its meaning. It helped understand and refine the nomenclature of the concepts (codes) that differed but were inserted in a similar context (e.g., increase the computational capability or take advantage of the latest hardware [33], [51]).…”

Section: Discussionmentioning

confidence: 99%

“…Source Practice 1 papers/posts Business understanding and validation [3], [2], [56], [57], [32], [58], [59], [60], [19], [61], [62] [52], [63], [64], [65], [66], [51], [18], [67], [68], [69] 2 papers/posts Identifying the right problem to solve [70], [32], [15] [71], [72], [73], [74], [75], [76], [77], [18], [67], [78], [68] 3 posts Verify how necessary is ML for the product [15] [79], [52], [80], [81], [82], [55], [64], [83], [84], [85], [86], [87], [66], [4], [51], [71], ...…”

Section: Idmentioning

confidence: 99%

Practices for Managing Machine Learning Products: a Multivocal Literature Review

alves¹,

Leite²,

Meirelles³

et al. 2023

Preprint

0

View full text Add to dashboard Cite

<p>Machine Learning (ML) has grown in popularity in the software industry due to its ability to solve complex problems. Developing ML Systems involves more uncertainty and risk because it requires identifying a business opportunity and managing the source code, data, and trained model. Our research aims to identify the existing practices used in the industry for building ML applications. The goal is to comprehend the orga- nizational complexity of adopting ML Systems. We conducted a Multivocal Literature Review (MLR) and used Grounded Theory (GT) to build a taxonomy with the practices applied to the ML System lifecycle from the industry and academic perspectives. We selected 41 posts from grey literature and 37 papers from scientific repositories. Following a systematic GT protocol, we mapped 91 practices, grouped in 6 core categories related to designing, developing, testing, and deploying ML Systems. The results can help organizations identify the gaps in their current ML processes and practices, and provide a roadmap for improving and optimizing their ML systems. The comprehensive taxonomy of practices developed in this research serves as a valuable tool for managers, practitioners, and researchers in the ML field, providing a clear and organized understanding of the complexity of managing ML systems.</p>

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