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.
Building Spark as Service in Cloud using YARN
Apache Spark is rapidly taking off in popularity as a new data processing framework. However - it can be daunting to install and run it. In this talk we will talk about the challenges of running Spark in the Cloud using YARN and how we have built Spark as a Service. We will also discuss about our learnings from building and operating this service in the AWS cloud and future directions.
We will talk about:
- Self managed spark clusters in cloud
- Using spot nodes in aws cloud
- Autoscaling spark application
- Running spark sql queries against existing hive metastore
- End user APIs and user interface for spark as service offering
Basic knowledge of spark, map reduce, cloud.
Bharath Bhushan: is working as Software Engg in Qubole. He is currently working on Spark offering. Earlier he has worked with Google (Page Speed team) and citrix.
Rajat Gupta: is working as Software Engg with Qubole. He is currently working on Spark offering. Earlier he has worked with Calypto and Cypress Semiconductors.