390,597 research outputs found
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
Technology Readiness Levels for Machine Learning Systems
The development and deployment of machine learning (ML) systems can be
executed easily with modern tools, but the process is typically rushed and
means-to-an-end. The lack of diligence can lead to technical debt, scope creep
and misaligned objectives, model misuse and failures, and expensive
consequences. Engineering systems, on the other hand, follow well-defined
processes and testing standards to streamline development for high-quality,
reliable results. The extreme is spacecraft systems, where mission critical
measures and robustness are ingrained in the development process. Drawing on
experience in both spacecraft engineering and ML (from research through product
across domain areas), we have developed a proven systems engineering approach
for machine learning development and deployment. Our "Machine Learning
Technology Readiness Levels" (MLTRL) framework defines a principled process to
ensure robust, reliable, and responsible systems while being streamlined for ML
workflows, including key distinctions from traditional software engineering.
Even more, MLTRL defines a lingua franca for people across teams and
organizations to work collaboratively on artificial intelligence and machine
learning technologies. Here we describe the framework and elucidate it with
several real world use-cases of developing ML methods from basic research
through productization and deployment, in areas such as medical diagnostics,
consumer computer vision, satellite imagery, and particle physics
ADAPTS: An Intelligent Sustainable Conceptual Framework for Engineering Projects
This paper presents a conceptual framework for the optimization of environmental sustainability in engineering projects, both for products and industrial facilities or processes. The main objective of this work is to propose a conceptual framework to help researchers to approach optimization under the criteria of sustainability of engineering projects, making use of current Machine Learning techniques. For the development of this conceptual framework, a bibliographic search has been carried out on the Web of Science. From the selected documents and through a hermeneutic procedure the texts have been analyzed and the conceptual framework has been carried out. A graphic representation pyramid shape is shown to clearly define the variables of the proposed conceptual framework and their relationships. The conceptual framework consists of 5 dimensions; its acronym is ADAPTS. In the base are: (1) the Application to which it is intended, (2) the available DAta, (3) the APproach under which it is operated, and (4) the machine learning Tool used. At the top of the pyramid, (5) the necessary Sensing. A study case is proposed to show its applicability. This work is part of a broader line of research, in terms of optimization under sustainability criteria.Telefónica Chair “Intelligence in Networks” of the University of Seville (Spain
Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning
Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R2 values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches
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