AzureML: Anatomy of a machine learning service

In this paper, we describe AzureML, a web service that provides a model authoring environment where data scientists can create machine learning models and publish them easily (http://www.azure.com/ml). In addition, AzureML provides several distinguishing features. These include: (a) collaboration, (b) versioning, (c) visual workflows, (d) external language support, (e) push-button operationalization, (f) monetization and (g) service tiers. We outline the system overview, design principles and lessons learned in building such a system.

[1]  Alan Edelman,et al.  Julia: A Fast Dynamic Language for Technical Computing , 2012, ArXiv.

[2]  Ian H. Witten,et al.  WEKA: a machine learning workbench , 1994, Proceedings of ANZIIS '94 - Australian New Zealnd Intelligent Information Systems Conference.

[3]  Yoav Freund,et al.  Large Margin Classification Using the Perceptron Algorithm , 1998, COLT.

[4]  P. McCullagh Regression Models for Ordinal Data , 1980 .

[5]  P. McCullagh,et al.  Generalized Linear Models , 1984 .

[6]  Jasper Snoek,et al.  Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.

[7]  Jianfeng Gao,et al.  Scalable training of L1-regularized log-linear models , 2007, ICML '07.

[8]  Prasoon Goyal,et al.  Local Deep Kernel Learning for Efficient Non-linear SVM Prediction , 2013, ICML.

[9]  Graham J. Williams,et al.  PMML: An Open Standard for Sharing Models , 2009, R J..

[10]  Francis R. Bach,et al.  Online Learning for Latent Dirichlet Allocation , 2010, NIPS.

[11]  Léon Bottou,et al.  Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.

[12]  Anil K. Jain Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[13]  Sebastian Nowozin,et al.  Decision Jungles: Compact and Rich Models for Classification , 2013, NIPS.

[14]  Antonio Criminisi,et al.  Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning , 2012, Found. Trends Comput. Graph. Vis..

[15]  Thore Graepel,et al.  Matchbox: large scale online bayesian recommendations , 2009, WWW '09.

[16]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[17]  Joseph M. Hellerstein,et al.  GraphLab: A New Framework For Parallel Machine Learning , 2010, UAI.

[18]  Yoshua Bengio,et al.  Algorithms for Hyper-Parameter Optimization , 2011, NIPS.

[19]  Tomaso Poggio,et al.  Everything old is new again: a fresh look at historical approaches in machine learning , 2002 .

[20]  Christopher J. C. Burges,et al.  From RankNet to LambdaRank to LambdaMART: An Overview , 2010 .

[21]  Colin Campbell,et al.  Bayes Point Machines , 2001, J. Mach. Learn. Res..

[22]  Scott Shenker,et al.  Spark: Cluster Computing with Working Sets , 2010, HotCloud.