Machine Learning, Distributed and Parallel Computing, and High-performance Computing are the themes for this year’s edition of Fifth Elephant.
The deadline for submitting a proposal is 15th June 2015
We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.
This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:
- Statistical Visualizations
- Unsupervised Learning
- Supervised Learning
- Semi-Supervised Learning
- Active Learning
- Reinforcement Learning
- Monte-carlo techniques and probabilistic programming
- Deep Learning
Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.
This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:
- Distributed and Parallel Computing
- Real Time Analytics and Stream Processing
- MapReduce and Graph Computing frameworks
- Kafka, Spark, Hadoop, MPI
- Stories of parallelizing sequential programs
- Cost/Security/Disaster Management of Data
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.
Apache Tez - Present and Future
Talk about the present and future of Apache Tez.
Apache Tez is a framework designed to build data-flow driven processing runtimes. Tez provides a scaffolding and library components that can be used to quickly build scalable and efficient data-flow centric engines. This talk will cover the journey of Tez from being a concept in the Apache Incubator to becoming the cornerstone of well-known projects such as Apache Hive and Apache Pig of the Hadoop ecosystem. I will then move on to the future of Tez on how it is improving to make it easier for data processing applications to be built to run in single-digit seconds and/or to scale to petabytes of data.
Rajesh Balamohan has been working on Hadoop for last couple of years and recently has been concentrating on Tez performance at scale.