The recently open-sourced platform called Marvin AI is helping B2W Digital data science teams to put some interesting projects into production, such as the dynamic pricing and fraud detection. The platform is helping B2W avoid challenges that are common in machine learning projects, like building a hypothesis prototype and putting it in production, enabling it to handle ever-increasing data sets, and supporting the necessary throughput of the largest e-commerce company in South America.
Marvin AI is made up of tools, libraries, an embedded server that exposes microservices to serve and manage models, and a very useful design pattern, DASFE, that simplifies the process of building new machine learning engines. In this 90-minute hands-on tutorial, one of the project architects and main open source contributor will explain all the core features of Marvin.
What do Practitioners Expect from the Meta-modeling Tools? A SurveyObeo
Modeling languages are defined with a meta-model, which are specified using the meta-modeling tools that produce the editors for specifying models in accordance with the meta-models. While many different meta-modeling tools have been available today, it is not yet clear what the expectations of practitioners are from the meta-modeling tools and what sort of challenges that practitioners face with. So, we designed and conducted a survey, which was responded by 103 practitioners from 24 different countries. The survey participants represent the different profiles of the population who differ in terms of the work industries, the problem domains, job positions, and years of experiences. Our survey investigates three important research questions, which essentially focus on the usage frequencies of the existing meta-modeling tools, practitioners’ expectations from the meta-modeling tools, and any challenges that practitioners face with. The survey questionnaire considers the notation, semantics, editor services, model-transformation, validation, testing, and composability requirements for meta-modeling tools.
The survey results lead to many interesting findings regarding the practical use of meta-modeling tools from different viewpoints. The survey also reveals many important challenges in each type of requirements. We strongly believe that the survey results are expected to be useful for anyone who consider developing their own DSMLs (domain-specific modeling languages) in understanding the top-used meta-modeling tools for different domains. Also, the tool vendors could use the survey results in learning the expectations of practitioners from the meta-modeling tools and any challenges encountered.
Assoc.Prof.Dr. Mert Ozkaya, Yeditepe University
Marvin AI - Uma plataforma de código aberto para implementar e gerenciar mode...Daniel Takabayashi, MSc
A plataforma, recentemente aberta, Marvin AI está ajudando equipes de ciência de dados da B2W Digital a colocar alguns projetos interessantes em produção. Como exemplos temos precificação dinâmica e detecção de fraudes.
A plataforma auxilia a B2W, evitando desafios que são comuns em projetos de aprendizado de máquina, como construir um protótipo de hipóteses e colocá-lo em produção. Isso permite que a equipe use conjuntos de dados cada vez maiores, e atinja a performance necessária na maior empresa de comércio eletrônico na América do Sul.
O Marvin AI é composto por ferramentas, bibliotecas, um servidor embutido que expõe micro-serviços para servir e gerenciar modelos, e um padrão de design altamente útil chamado DASFE, que simplifica o processo de construção de novos mecanismos de aprendizagem de máquina.
Nessa palestra, serão apresentadas as principais features da plataforma open source Marvin AI, além disso, serão discutidas todas as decisões arquiteturais implementadas para suportar projetos de machine learning na B2W Digital.
ACM Chicago March 2019 meeting: Software Engineering and AI - Prof. Tao Xie, ...ACM Chicago
Join us as Tao Xie, Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign and ACM Distinguished Speaker, talks about Intelligent Software Engineering: Synergy between AI and Software Engineering. This is a joint meeting hosted by Chicago Chapter ACM / Loyola University Computer Science Department.
What do Practitioners Expect from the Meta-modeling Tools? A SurveyObeo
Modeling languages are defined with a meta-model, which are specified using the meta-modeling tools that produce the editors for specifying models in accordance with the meta-models. While many different meta-modeling tools have been available today, it is not yet clear what the expectations of practitioners are from the meta-modeling tools and what sort of challenges that practitioners face with. So, we designed and conducted a survey, which was responded by 103 practitioners from 24 different countries. The survey participants represent the different profiles of the population who differ in terms of the work industries, the problem domains, job positions, and years of experiences. Our survey investigates three important research questions, which essentially focus on the usage frequencies of the existing meta-modeling tools, practitioners’ expectations from the meta-modeling tools, and any challenges that practitioners face with. The survey questionnaire considers the notation, semantics, editor services, model-transformation, validation, testing, and composability requirements for meta-modeling tools.
The survey results lead to many interesting findings regarding the practical use of meta-modeling tools from different viewpoints. The survey also reveals many important challenges in each type of requirements. We strongly believe that the survey results are expected to be useful for anyone who consider developing their own DSMLs (domain-specific modeling languages) in understanding the top-used meta-modeling tools for different domains. Also, the tool vendors could use the survey results in learning the expectations of practitioners from the meta-modeling tools and any challenges encountered.
Assoc.Prof.Dr. Mert Ozkaya, Yeditepe University
Marvin AI - Uma plataforma de código aberto para implementar e gerenciar mode...Daniel Takabayashi, MSc
A plataforma, recentemente aberta, Marvin AI está ajudando equipes de ciência de dados da B2W Digital a colocar alguns projetos interessantes em produção. Como exemplos temos precificação dinâmica e detecção de fraudes.
A plataforma auxilia a B2W, evitando desafios que são comuns em projetos de aprendizado de máquina, como construir um protótipo de hipóteses e colocá-lo em produção. Isso permite que a equipe use conjuntos de dados cada vez maiores, e atinja a performance necessária na maior empresa de comércio eletrônico na América do Sul.
O Marvin AI é composto por ferramentas, bibliotecas, um servidor embutido que expõe micro-serviços para servir e gerenciar modelos, e um padrão de design altamente útil chamado DASFE, que simplifica o processo de construção de novos mecanismos de aprendizagem de máquina.
Nessa palestra, serão apresentadas as principais features da plataforma open source Marvin AI, além disso, serão discutidas todas as decisões arquiteturais implementadas para suportar projetos de machine learning na B2W Digital.
ACM Chicago March 2019 meeting: Software Engineering and AI - Prof. Tao Xie, ...ACM Chicago
Join us as Tao Xie, Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign and ACM Distinguished Speaker, talks about Intelligent Software Engineering: Synergy between AI and Software Engineering. This is a joint meeting hosted by Chicago Chapter ACM / Loyola University Computer Science Department.
London atlassian meetup 31 jan 2016 jira metrics-extract slidesRudiger Wolf
Slides for talk given to London Atlassian User Group Jan 2017. How to get started with Python to extract data from Jira and produce charts for your Agile team.
In this talk, I explore what productivity means to software developers, how we might track the value that is delivered in software produced by developers and how we might begin to think about measuring the productive delivery of effective software.
Keynote at International Conference on Performance Engineering (ICPE) 2020.
This presentation describes some of the Open Source Ai projects we are working at the Center for Open Source, Data and AI Technologies (CODAIT), including Model Asset Exchange (MAX), Fabric for Deep Learning (FfDL) and Jupyter Enterprise Gateway.
ICONIQ Analytics: The Modern Developer Technology StackChristine Edmonds
Earlier this year, ICONIQ Growth¹ performed an in-depth study of the developer technology stack to help us better understanding emerging trends, most commonly adopted tools, and key questions assessed during decision making processes.
Marvin is an ambitious open source project that focus on helping data science team members to deliver complex solutions supported by a high-scale, low-latency, language agnostic and standardized architecture platform, while simplifying the process of exploitation and modeling.
Build model-based applications in a robust way is not trivial, one is required to have knowledge in advanced areas of sciences like computing, statistics and math. Marvin aims to abstract the complexities involved in the creation process of a scalable, high available, interoperable and maintainable software. By doing that it allows the data scientist to focus on the core value of their applications.
Marvin é um ambicioso projeto de código aberto que se concentra em ajudar equipes a entregar soluções de machine learning de maneira ágil. A plataforma oferece uma arquitetura padronizada e agnóstica de linguagem, de alta escala e baixa latência enquanto simplifica o processo de exploração e modelagem de projetos de IA.
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In this talk, I explore what productivity means to software developers, how we might track the value that is delivered in software produced by developers and how we might begin to think about measuring the productive delivery of effective software.
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