Abstract
This qualitative phenomenological study investigated machine learning (ML) model deployment challenges during the ML lifecycle using the theoretical framework of the technology acceptance model (TAM). Researchers have designed several frameworks for understanding the ML lifecycle, but those frameworks remain untested, and many ML model deployments still fail. The study’s central research question asked, what challenges do organizations face when deploying ML models in production environments? The phenomenological research design identified users’ perceptions and lived experiences deploying ML models in production environments. Data were collected via semi structured interviews with 15 ML experts. The phenomenon from the interviews was described in textural, structural, and textural-structural descriptions of participants’ lived experiences.
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