Add to ORKGDeploying machine learning (ML) models to production with the same level of rigor and automation as traditional software systems has shown itself to be a non-trivial task, requiring extra care and infrastructure to deal with the additional challenges. Although many studies focus on adapting ML software engineering (SE) approaches and techniques, few studies have summarized the status and challenges of operationalizing ML models. Model operationalization encompasses all steps after model training and evaluation, including packaging the model in a format appropriate for deployment, publishing to a model registry or storage, integrating the model into a broader software system, serving, and monitoring. This study is the first systematic litera...
This thesis presents a solution architecture for productizing machine learning models in an enterpri...
Over the past few decades, the substantial growth in enterprise-data availability and the advancemen...
The increased availability of data gives rise to the use of machine learning methods for purposes li...
Machine Learning (ML) has grown in popularity in the software industry due to its ability to solve c...
With the evolution of algorithms and solutions in the artificial intelligence field, new and modern ...
Machine learning has become a key driver for technological advancement in the last decade on the bac...
In the last couple of years we have witnessed an enormous increase of machine learning (ML) applicat...
Context: With the advent of Machine Learning (ML) and especially Deep Learning (DL) technology, comp...
This chapter discusses how to build production-ready machine learning systems. There are several cha...
Developing machine learning (ML) models can be seen as a process similar to the one established for ...
As the use of machine learning techniques by organisations has become more common, the need for soft...
Nowadays, machine learning (ML) is an integral component in a wide range of areas, including softwar...
The increasing reliance on applications with machine learning (ML) components calls for mature engin...
In many machine learning projects, the lack of an effective monitoring system is a worrying issue. T...
Thanks to many breakthroughs in neural network techniques, machine learning is widely applied in man...
This thesis presents a solution architecture for productizing machine learning models in an enterpri...
Over the past few decades, the substantial growth in enterprise-data availability and the advancemen...
The increased availability of data gives rise to the use of machine learning methods for purposes li...
Machine Learning (ML) has grown in popularity in the software industry due to its ability to solve c...
With the evolution of algorithms and solutions in the artificial intelligence field, new and modern ...
Machine learning has become a key driver for technological advancement in the last decade on the bac...
In the last couple of years we have witnessed an enormous increase of machine learning (ML) applicat...
Context: With the advent of Machine Learning (ML) and especially Deep Learning (DL) technology, comp...
This chapter discusses how to build production-ready machine learning systems. There are several cha...
Developing machine learning (ML) models can be seen as a process similar to the one established for ...
As the use of machine learning techniques by organisations has become more common, the need for soft...
Nowadays, machine learning (ML) is an integral component in a wide range of areas, including softwar...
The increasing reliance on applications with machine learning (ML) components calls for mature engin...
In many machine learning projects, the lack of an effective monitoring system is a worrying issue. T...
Thanks to many breakthroughs in neural network techniques, machine learning is widely applied in man...
This thesis presents a solution architecture for productizing machine learning models in an enterpri...
Over the past few decades, the substantial growth in enterprise-data availability and the advancemen...
The increased availability of data gives rise to the use of machine learning methods for purposes li...