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TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries
[article]
2015
arXiv
pre-print
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These and many other applications can be supported by Predictive Analytic Queries (PAQs). ...
The result is TuPAQ, a component of the MLbase system, which solves the PAQ planning problem with comparable quality to exhaustive strategies but an order of magnitude more efficiently than the standard ...
Recht who provided valuable ideas about derivative-free optimization and feedback, and Shivaram Venkataraman, Peter Bailis, Alan Fekete, Dan Crankshaw, Sanjay Krishnan, Xinghao Pan, and Kevin Jamieson for ...
arXiv:1502.00068v2
fatcat:l5ane47jazgq7cm3w7wh5cmho4
Automating model search for large scale machine learning
2015
Proceedings of the Sixth ACM Symposium on Cloud Computing - SoCC '15
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by large-scale datasets. ...
but an order of magnitude more efficiently than the standard baseline approach. ...
ideas about derivative-free optimization and feedback, and Shivaram Venkataraman, Peter Bailis, Alan Fekete, Dan Crankshaw, Sanjay Krishnan, Xinghao Pan, Kevin Jamieson, and our Shepherd, Siddhartha Sen, for ...
doi:10.1145/2806777.2806945
dblp:conf/cloud/SparksTHFJK15
fatcat:y4mzheh2ejf5fmimcauahaif5i
Mining Big Data Using Modified Induction Tree Approach
2016
International Journal of Intelligent Engineering and Systems
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Induction Tree, for example, C4.5 is the most favored technique since it functions well under any dataset set being utilized. ...
Another part called TuPAQ (Training-upheld Predictive Analytic Query Planner) -one of late presented, which expands on the underlying thought of ML Optimizer. ...
So it is also knows ad large-scale computational intensive scientific exploration. S4: It is an Apache Incubator project. ...
doi:10.22266/ijies2016.0630.03
fatcat:4zhmb4aqtffndobb6mbvu5zbbm
A survey of open source tools for machine learning with big data in the Hadoop ecosystem
2015
Journal of Big Data
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Abstract With an ever-increasing amount of options, the task of selecting machine learning tools for big data can be difficult. ...
To that end, this paper provides a list of criteria for making selections along with an analysis of the advantages and drawbacks of each. ...
A new component called TuPAQ (Training-supported Predictive Analytic Query Planner) [137] was recently introduced, which builds on the initial idea of ML Optimizer. ...
doi:10.1186/s40537-015-0032-1
fatcat:zgcsiokrynfhzbmaudqf7rcll4
AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
[article]
2022
arXiv
pre-print
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Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. ...
We also develop a conceptual framework throughout the review, augmented by each topic, to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. ...
Notably, an early forerunner of this movement is MLbase [219] , which is supported by a MAB-based CASH-solver called 'Training supported Predictive Analytic Queries' (TuPAQ) [347] . ...
arXiv:2012.12600v2
fatcat:6rj4ubhcjncvddztjs7tql3itq