Parallelizing Machine Learning as a service for the end-user
نویسندگان
چکیده
منابع مشابه
Interactive Machine Learning for End-User Innovation
User interaction with intelligent systems need not be limited to interaction where pre-trained software has intelligence “baked in.” End-user training, including interactive machine learning (IML) approaches, can enable users to create and customise systems themselves. We propose that the user experience of these users is worth considering. Furthermore, the user experience of system developers—...
متن کاملEffective End-User Interaction with Machine Learning
End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems...
متن کاملStatistics and Machine Learning Techniques for Real End-User Experience
Real End-User Experience (RUE) is a monitoring approach that aims to measure the end-user experience by providing information on availability, response time, and reliability of the real used IT services. The response time of each user transaction is measured by an analysis of the network communication flows. Several performance metrics get archived to monitor RUE over time. An abstract, general...
متن کاملA Spatial EA Framework for Parallelizing Machine Learning Methods
The scalability of machine learning (ML) algorithms has become increasingly important due to the ever increasing size of datasets and increasing complexity of the models induced. Standard approaches for dealing with this issue generally involve developing parallel and distributed versions of the ML algorithms and/or reducing the dataset sizes via sampling techniques. In this paper we describe a...
متن کاملEnd-User Machine Learning in Music Composition and Performance
We discuss our work creating the Wekinator software for end-user interactive machine learning, and we outline five key findings pertaining to our observations of its use in music composition and performance.
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Future Generation Computer Systems
سال: 2020
ISSN: 0167-739X
DOI: 10.1016/j.future.2019.11.042