2,030 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
Deep Learning for Mobile Multimedia: A Survey
Deep Learning (DL) has become a crucial technology for multimedia computing. It offers a powerful instrument to automatically produce high-level abstractions of complex multimedia data, which can be exploited in a number of applications, including object detection and recognition, speech-to- text, media retrieval, multimodal data analysis, and so on. The availability of affordable large-scale parallel processing architectures, and the sharing of effective open-source codes implementing the basic learning algorithms, caused a rapid diffusion of DL methodologies, bringing a number of new technologies and applications that outperform, in most cases, traditional machine learning technologies. In recent years, the possibility of implementing DL technologies on mobile devices has attracted significant attention. Thanks to this technology, portable devices may become smart objects capable of learning and acting. The path toward these exciting future scenarios, however, entangles a number of important research challenges. DL architectures and algorithms are hardly adapted to the storage and computation resources of a mobile device. Therefore, there is a need for new generations of mobile processors and chipsets, small footprint learning and inference algorithms, new models of collaborative and distributed processing, and a number of other fundamental building blocks. This survey reports the state of the art in this exciting research area, looking back to the evolution of neural networks, and arriving to the most recent results in terms of methodologies, technologies, and applications for mobile environments
OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System
Automated machine learning (AutoML) seeks to build ML models with minimal
human effort. While considerable research has been conducted in the area of
AutoML in general, aiming to take humans out of the loop when building
artificial intelligence (AI) applications, scant literature has focused on how
AutoML works well in open-environment scenarios such as the process of training
and updating large models, industrial supply chains or the industrial
metaverse, where people often face open-loop problems during the search
process: they must continuously collect data, update data and models, satisfy
the requirements of the development and deployment environment, support massive
devices, modify evaluation metrics, etc. Addressing the open-environment issue
with pure data-driven approaches requires considerable data, computing
resources, and effort from dedicated data engineers, making current AutoML
systems and platforms inefficient and computationally intractable.
Human-computer interaction is a practical and feasible way to tackle the
problem of open-environment AI. In this paper, we introduce OmniForce, a
human-centered AutoML (HAML) system that yields both human-assisted ML and
ML-assisted human techniques, to put an AutoML system into practice and build
adaptive AI in open-environment scenarios. Specifically, we present OmniForce
in terms of ML version management; pipeline-driven development and deployment
collaborations; a flexible search strategy framework; and widely provisioned
and crowdsourced application algorithms, including large models. Furthermore,
the (large) models constructed by OmniForce can be automatically turned into
remote services in a few minutes; this process is dubbed model as a service
(MaaS). Experimental results obtained in multiple search spaces and real-world
use cases demonstrate the efficacy and efficiency of OmniForce
Relay: A New IR for Machine Learning Frameworks
Machine learning powers diverse services in industry including search,
translation, recommendation systems, and security. The scale and importance of
these models require that they be efficient, expressive, and portable across an
array of heterogeneous hardware devices. These constraints are often at odds;
in order to better accommodate them we propose a new high-level intermediate
representation (IR) called Relay. Relay is being designed as a
purely-functional, statically-typed language with the goal of balancing
efficient compilation, expressiveness, and portability. We discuss the goals of
Relay and highlight its important design constraints. Our prototype is part of
the open source NNVM compiler framework, which powers Amazon's deep learning
framework MxNet
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