Machine learning 2.0 Engineering data driven AI products
نویسندگان
چکیده
ML 2.0: In this paper, we propose a paradigm shift from the current practice of creating machine learning models that requires months-long discovery, exploration and “feasibility report” generation, followed by re-engineering for deployment, in favor of a rapid 8 week long process of development, understanding, validation and deployment that can executed by developers or subject matter experts (non-ML experts) using reusable APIs. It accomplishes what we call a “minimum viable data-driven model,” delivering a ready-to-use machine learning model for problems that haven’t been solved before using machine learning. We provide provisions for the refinement and adaptation of the “model," with strict enforcement and adherence to both the scaffolding/abstractions and the process. We imagine that this will bring forth a second phase in machine learning, in which discovery is subsumed by more targeted goals of delivery and impact.
منابع مشابه
Ki 2009
This conference aims to gather researchers and developers from academic fields and industries worldwide to share their research results covering all aspects of artificial intelligence. It will be an international forum for discussions on recent progress in different fields of artificial intelligence including, but not limited to: Agents, AI and engineering, Cognitive modeling, Cognitive systems...
متن کاملData-Driven Approaches to Improve the Quality of Clinical Processes: A Systematic Review
Background: Considering the emergence of electronic health records and their related technologies, an increasing attention is paid to data driven approaches like machine learning, data mining, and process mining. The aim of this paper was to identify and classify these approaches to enhance the quality of clinical processes. Methods: In order to determine the knowledge related to the research ...
متن کاملEnhancing Learning from Imbalanced Classes via Data Preprocessing: A Data-Driven Application in Metabolomics Data Mining
This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...
متن کاملDebt Collection Industry: Machine Learning Approach
Businesses are increasingly interested in how big data, artificial intelligence, machine learning, and predictive analytics can be used to increase revenue, lower costs, and improve their business processes. In this paper, we describe how we have developed a data-driven machine learning method to optimize the collection process for a debt collection agency. Precisely speaking, we create a frame...
متن کاملSemi-Automatic Terminology Ontology Learning Based on Topic Modeling
Ontologies provide features like a common vocabulary, reusability, machine-readable content, and also allows for semantic search, facilitate agent interaction and ordering & structuring of knowledge for the Semantic Web (Web 3.0) application. However, the challenge in ontology engineering is automatic learning, i.e., the there is still a lack of fully automatic approach from a text corpus or da...
متن کامل