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Open AccessProceedings ArticleDOI

Quick Access: Building a Smart Experience for Google Drive

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TLDR
The development of Quick Access illustrates many general challenges and constraints associated with practical machine learning such as protecting user privacy, working with data services that are not designed with machine learning in mind, and evolving product definitions.
Abstract
Google Drive is a cloud storage and collaboration service used by hundreds of millions of users around the world. Quick Access is a new feature in Google Drive that surfaces the most relevant documents when a user visits the home screen. Our metrics show that users locate their documents in half the time with this feature compared to previous approaches. The development of Quick Access illustrates many general challenges and constraints associated with practical machine learning such as protecting user privacy, working with data services that are not designed with machine learning in mind, and evolving product definitions. We believe that the lessons learned from this experience will be useful to practitioners tackling a wide range of applied machine learning problems.

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Book ChapterDOI

A taxonomy of software engineering challenges for machine learning systems : An empirical investigation

TL;DR: This study explored the development of machine learning systems from six different companies across various domains and identified main software engineering challenges, mapped into a proposed taxonomy that depicts the evolution of use of ML components in software-intensive system in industrial settings.
Proceedings ArticleDOI

Software Engineering Challenges of Deep Learning

TL;DR: The challenges identified in this paper can be used to guide future research by the software engineering and DL communities and could enable a large number of companies to start taking advantage of the high potential of the DL technology.
Proceedings ArticleDOI

TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank

TL;DR: This work introduces TensorFlow Ranking, the first open source library for solving large-scale ranking problems in a deep learning framework, which is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics in the learning- to-rank setting.
Proceedings ArticleDOI

TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank

TL;DR: TensorFlow Ranking as discussed by the authors is an open source library for solving large-scale ranking problems in a deep learning framework, which is highly configurable and provides easy-to-use APIs to support different scoring mechanisms, loss functions and evaluation metrics.
Proceedings ArticleDOI

Data Management Challenges for Deep Learning

TL;DR: A case study approach is employed to explore the data management issues faced by practitioners across various domains when they use real-world data for training and deploying deep learning models.
References
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Proceedings Article

ImageNet Classification with Deep Convolutional Neural Networks

TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI

Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book

Deep Learning

TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI

TensorFlow: a system for large-scale machine learning

TL;DR: TensorFlow as mentioned in this paper is a machine learning system that operates at large scale and in heterogeneous environments, using dataflow graphs to represent computation, shared state, and the operations that mutate that state.
Journal ArticleDOI

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
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How do you get experience in machine learning?

We believe that the lessons learned from this experience will be useful to practitioners tackling a wide range of applied machine learning problems.