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.
Scrap Your MapReduce - Introduction to Apache Spark
Introduction to Apache Spark, compare and contrast it with MapReduce programming model, see what Apache Spark has to offer, where it shines, how to use it via real life examples.
Remember the last time you tried to write a MapReduce job(obviously something non trivial than a word count)? Did you feel how this could be done in a better way?
Did you wonder how life will be much simple if you had to code like doing collection operations and hence being transparent to its distributed nature? Did you want/hope for more performant/low latency jobs?
Well, seems like you are in luck. This talk will get you started with Apache Spark, and see where it shines, why to use it, how to use it.
We’ll be covering aspects like testability, maintainability, conciseness of the code, and some features like iterative processing, optional in-memory caching and others.
We will see how Spark, being just a cluster computing engine, abstracts the underlying distributed storage, and cluster management aspects, giving us a uniform interface to consume/process/query the data.
We will explore the basic abstraction of RDD which gives us so many awesome features making Spark a very good choice for your big data applications.
We will see this through some non trivial code examples.
Rahul is an Application developer with Thoughtworks. Rahul has worked with technologies ranging from Scala, Java, Ruby. He has experience in builiding web applications to solving big data problems. Rahul has special interest in Scala, Apache Spark. He loves functional programming.