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Scalable End-to-End ML Platforms: from AutoML to Self-serve

Scalable End-to-End ML Platforms: from AutoML to Self-serve

27 February 2023
I. Markov
P. Apostolopoulos
Mia Garrard
Tianyu Qie
Yin Huang
Tanvi Gupta
Anika Li
Cesar Cardoso
George Han
Ryan Maghsoudian
Norm Zhou
    LRM
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Papers citing "Scalable End-to-End ML Platforms: from AutoML to Self-serve"

4 / 4 papers shown
Title
Looper: An end-to-end ML platform for product decisions
Looper: An end-to-end ML platform for product decisions
I. Markov
Hanson Wang
Nitya Kasturi
Shaun Singh
Szeto Wai Yuen
...
Michael Belkin
Sal Uryasev
Sam Howie
E. Bakshy
Norm Zhou
OffRL
18
15
0
14 Oct 2021
A Workflow for Offline Model-Free Robotic Reinforcement Learning
A Workflow for Offline Model-Free Robotic Reinforcement Learning
Aviral Kumar
Anika Singh
Stephen Tian
Chelsea Finn
Sergey Levine
OffRL
138
84
0
22 Sep 2021
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on
  Open Problems
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Sergey Levine
Aviral Kumar
George Tucker
Justin Fu
OffRL
GP
321
1,662
0
04 May 2020
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
Nick Erickson
Jonas W. Mueller
Alexander Shirkov
Hang Zhang
Pedro Larroy
Mu Li
Alex Smola
LMTD
84
576
0
13 Mar 2020
1