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SUMMARY:IC Colloquium :  Scalable Systems for Fast and Easy Machine Learni
 ng
DTSTART:20170410T101500
DTEND:20170410T113000
DTSTAMP:20260414T221443Z
UID:cc47babc69764ea290056a07eef79d2f2b562fea9dd99aacf34f29c2
CATEGORIES:Conferences - Seminars
DESCRIPTION:By : Shivaram Venkataraman - UC Berkeley\nIC Faculty candidate
 \n\nAbstract :\nMachine learning models trained on massive datasets power 
 a number of applications\; from machine translation to detecting supernova
 e in astrophysics. However the end of Moore’s law and the shift towards 
 distributed computing architectures presents many new challenges for build
 ing and executing such applications in a scalable fashion. \n \nIn this 
 talk I will present my research on systems that make it easier to develop 
 new machine learning applications and scale them while achieving high perf
 ormance. I will first present programming models that let users easily bui
 ld distributed machine learning applications. Next\, I will show how we ca
 n exploit the structure of machine learning workloads to build low-overhea
 d performance models that can help users understand scalability and simpli
 fy large scale deployments. Finally\, I will describe scheduling technique
 s that can improve scalability and achieve high performance when using dis
 tributed data processing frameworks.\n\nBio :\nShivaram Venkataraman is a 
 PhD Candidate at the University of California\, Berkeley and is advised by
  Mike Franklin and Ion Stoica. His research interests are in designing sys
 tems and algorithms for large scale data processing and machine-learning. 
 He is a recipient of the Siebel Scholarship and best-of-conference citatio
 ns at VLDB and KDD. Before coming to Berkeley\, he completed his M.S at th
 e University of Illinois\, Urbana-Champaign.\n\nMore information\n 
LOCATION:BC 420 https://plan.epfl.ch/?room==BC%20420
STATUS:CONFIRMED
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