Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned

REEF: Retainable Evaluator Execution Framework

In this demo proposal, we describe REEF, a framework that makes it easy to implement scalable, fault-tolerant runtime environments for a range of computational models. We will demonstrate diverse workloads, including extract-transform-load MapReduce jobs, iterative machine learning algorithms, and ad-hoc declarative query processing. At its core, REEF builds atop YARN (Apache Hadoop 2’s resource manager) to provide retainable hardware resources with lifetimes that are decoupled from those of computational tasks. This allows us to build persistent (cross-job) caches and clusterwide services, but, more importantly, supports high-performance iterative graph processing and machine learning algorithms. Unlike existing systems, REEF aims for composability of jobs across computational models, providing significant performance and usability gains, even with legacy code. REEF includes a library of interoperable data management primitives optimized for communication and data movement (which are distinct from storage locality). The library also allows REEF applications to access external services, such as user-facing relational databases. We were careful to decouple lower levels of REEF from the data models and semantics of systems built atop it. The result was two new standalone systems: Tang, a configuration manager and dependency injector, and Wake, a state-of-the-art event-driven programming and data movement framework. Both are language independent, allowing REEF to bridge the JVM and.NET. 1

Матрична структура оцінки організаційно-технологічних можливостей будівельної організації

Радкевич, А. В. Матричная структура оценки организационно- технологических возможностей строительной организации / А. В. Радкевич, С. А. Яковлев, А. А. Матусевич // Вісн. Дніпропетр. нац. ун-ту залізн. трансп. ім. акад. В. Лазаряна. — Дніпропетровськ, 2003. — Вип. 2. — С. 191—196.RU: Изложена методика матричного исчисления, оценки, организационно-технологических возможностей строительной организации.UK: Викладена методика матричного розрахунку, оцінки, організаційно-технологічних можливостей будівельної організації.EN: The methods of matrix calculation, organizational and technological opportunities of the building company is stated in the paper