Minimum Viable Model Estimates for Machine Learning Projects
Prioritization of machine learning projects requires estimates of both the potential ROI of the business case and the technical difficulty of building a model with the required characteristics. In this work we present a technique for estimating the minimum required performance characteristics of a predictive model given a set of information about how it will be used. This technique will result in robust, objective comparisons between potential projects. The resulting estimates will allow data scientists and managers to evaluate whether a proposed machine learning project is likely to succeed before any modelling needs to be done. The technique has been implemented into the open source application MinViME (Minimum Viable Model Estimator) which can be installed via the PyPI python package management system, or downloaded directly from the GitHub repository. Available at https://github.com/john-hawkins/MinViME
Code (1)
Tasks
BIG-bench Machine LearningManagementmodelSimilar Papers 제목 키워드 기반
Improving Stability Estimates in Adversarial Explainable AI through Alternate Search Methods
Advances in the effectiveness of machine learning models have come at the cost of enormous complexity resulting in a poor understanding of how they function. Local surrogate methods have been used to approximate the work…
Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural Networks
To protect tropical forest biodiversity, we need to be able to detect it reliably, cheaply, and at scale. Automated species detection from passively recorded soundscapes via machine-learning approaches is a promising tec…
Data AugmentationManagementTransfer LearningPareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD)
This paper introduces the Pareto Data Framework, an approach for identifying and selecting the Minimum Viable Data (MVD) required for enabling machine learning applications on constrained platforms such as embedded syste…
Decision MakingQuantizationUser Story Tutor (UST) to Support Agile Software Developers
User Stories record what must be built in projects that use agile practices. User Stories serve both to estimate effort, generally measured in Story Points, and to plan what should be done in a Sprint. Therefore, it is e…
Text ClassificationFull-Stack Filters to Build Minimum Viable CNNs
Deep convolutional neural networks (CNNs) are usually over-parameterized, which cannot be easily deployed on edge devices such as mobile phones and smart cameras. Existing works used to decrease the number or size of req…
Diversity