Tag Archives:

MLlib: Machine Learning in Apache Spark

Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar
Journal of Machine Learning Research, 17 (34), Apr. 2016.

Tags: Machine Learning, MLlib, spark

SparkNet

Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this … Continue reading →

SparkNet: Training Deep Networks on Spark

Philipp Moritz, Robert Nishihara, Ion Stoica, Michael Jordan
International Conference on Learning Representations (ICLR), May. 2016.

Tags: deep learning, distributed machine learning, Machine Learning, spark

KeystoneML

KeystoneML is a research project exploring techniques to simplify the construction of large scale, end-to-end, machine learning pipelines. KeystoneML is designed around … Continue reading →

Automating Model Search for Large Scale Machine Learning

Evan Sparks, Ameet Talwalkar, Daniel Haas, Michael Franklin, Michael Jordan, Tim Kraska
SoCC 2015, Aug. 2015.

Tags: Declarative ML, Machine Learning, MLbase, TuPAQ

The missing piece in complex analytics: Low latency, scalable model management and serving with Velox

Dan Crankshaw, Peter Bailis, Joseph Gonzalez, Haoyuan Li, Zhao Zhang, Michael Franklin, Ali Ghodsi, Michael Jordan
Conference on Innovative Data Systems Research (CIDR), Jan. 2015.

Tags: BDAS, Machine Learning, Real-time, Velox

Velox: Models in Action

To support complex data-intensive applications such as personalized recommendations, targeted advertising, and intelligent services, the data management community has focused … Continue reading →

Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning

Barzan Mozafari, Purna Sarkar, Michael Franklin, Michael Jordan, Sam Madden
VLDB 2015 (PVLDB Vol. 8, No. 2), Aug. 2015.

Tags: active learning, amp, crowdsourcing, databases, Machine Learning

Carat: Collaborative Energy Diagnosis for Mobile Devices

Adam Oliner, Anand Padmanabha Iyer, Ion Stoica, Eemil Lagerspetz, Sasu Tarkoma
SenSys 2013, Nov. 2013.

Tags: battery, carat, cloud, diagnosis, energy, Machine Learning, mobile

MLbase: A Distributed Machine-learning System

Tim Kraska, Ameet Talwalkar, John Duchi, Rean Griffith, Michael Franklin, Michael Jordan
CIDR 2013, Jan. 2013.

Tags: Declarative ML, distributed machine learning, Machine Learning, MLbase