Machine learning has recently emerged as a powerful technique to increase operational efficiency or to develop new value propositions. However, the translation of a prediction algorithm into an operationally usable machine learning model is a time-consuming and in various ways challenging task. In this work, we target to systematically elicit the challenges in deployment and operation to enable broader practical dissemination of machine learning applications. To this end, we first identify relevant challenges with a structured literature analysis. Subsequently, we conduct an interview study with machine learning practitioners across various industries, perform a qualitative content analysis, and identify challenges organized along three dis...
Background: The rapid advancement of Machine Learning (ML) across various domains has led to its wid...
abstract: Only an Executive Summary of the project is included. The goal of this project is to deve...
Title: Exploring the technology of machine learning to improve the demand forecasting Authors: Vikto...
Machine learning has recently emerged as a powerful technique to increase operational efficiency or ...
Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maint...
The purpose of this research is to understand the main managerial challenges that arise in the conte...
As the application of machine learning (ML) algorithms becomes more widespread, governmental organis...
Machine learning has become a key driver for technological advancement in the last decade on the bac...
Predictions computed by supervised machine learning models play a crucial role in a variety of innov...
Deploying machine learning (ML) models to production with the same level of rigor and automation as ...
Abstract As the level of digitization in industrial environments increases, companies are striving t...
Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled sys...
Machine learning (ML) techniques are rapidly evolving, both in academia and practice. However, enter...
Machine Learning (ML) has grown in popularity in the software industry due to its ability to solve c...
Demand for machine learning is ever-growing in today’s business. Situated at the convergence point o...
Background: The rapid advancement of Machine Learning (ML) across various domains has led to its wid...
abstract: Only an Executive Summary of the project is included. The goal of this project is to deve...
Title: Exploring the technology of machine learning to improve the demand forecasting Authors: Vikto...
Machine learning has recently emerged as a powerful technique to increase operational efficiency or ...
Organizations rely on machine learning engineers (MLEs) to operationalize ML, i.e., deploy and maint...
The purpose of this research is to understand the main managerial challenges that arise in the conte...
As the application of machine learning (ML) algorithms becomes more widespread, governmental organis...
Machine learning has become a key driver for technological advancement in the last decade on the bac...
Predictions computed by supervised machine learning models play a crucial role in a variety of innov...
Deploying machine learning (ML) models to production with the same level of rigor and automation as ...
Abstract As the level of digitization in industrial environments increases, companies are striving t...
Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled sys...
Machine learning (ML) techniques are rapidly evolving, both in academia and practice. However, enter...
Machine Learning (ML) has grown in popularity in the software industry due to its ability to solve c...
Demand for machine learning is ever-growing in today’s business. Situated at the convergence point o...
Background: The rapid advancement of Machine Learning (ML) across various domains has led to its wid...
abstract: Only an Executive Summary of the project is included. The goal of this project is to deve...
Title: Exploring the technology of machine learning to improve the demand forecasting Authors: Vikto...