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.
Instrumenting your kafka & storm pipeline
tips to design your stream processing setup.
what all can go wrong, how to instrument it.
introduction to a production setup that handles billions of events per week through our home-grown apache kafka client, process the stream with storm - and then aggregate into postgres. will also share pro’s and con’s of building our own kafka client as opposed to re-using one.
Bosky (@bhaskerkode) leads a product engg team at Helpshift & works on erlang, clojure and golang.
Built a kafka producer/micro-service used in production at helpshift, layer, and several other companies.
( Recommended by Apache kafka https://cwiki.apache.org/confluence/display/KAFKA/Clients )
Using storm in production for sentiment analysis, topic extraction, naive bayes classification, etc.
Eager to learn more about best practices in storm deployment/management. Incidentally another part that feeds into this system is in golang, uses the Shopify kafka producer.