The Fifth Elephant 2015

A conference on data, machine learning, and distributed and parallel computing

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.

Track 1: Discovering Insights and Driving Decisions

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.

Track 2: Speed at Scale

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

Commitment to Open Source

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.

Workshops

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.

Hosted by

All about data science and machine learning

Vinodh Kumar R

@vinodhkumarr

Building a E-commerce search engine: Challenges, insights and approaches

Submitted Jun 15, 2015

The objective of the talk is to motivate the problems and challenges of e-commerce search and provides insights and approaches on how one can go about building a world class product search engine.

Outline

In this talk, we go into the details of what goes behind the scenes in building a state-of-the-art E-Commerce search engine. Over the last decade, there is a huge surge in the amounts of (text and even non-text) data that is not only being collected and stored but also being accessed - e.g. web documents (e.g Google), user activities (e.g. facebook, pinterest, ads) and search engine applications have become one critical way to access this data on demand. While scale of the data is a inherent problem to be addressed while building search engines, e-commerce applications present its own set of challenges that make building an e-commerce search engine unique and markedly different from traditional search applications. In this talk, we will analyze the challenges, gather insights from this problem space and go over a few approaches on how to go about building a world-class product search engine

Requirements

Used google :P

Speaker bio

Vinodh Kumar is the CTO and M.D of BloomReach India. At BloomReach, Vinodh Kumar has been leading the engineering and development of BloomReach’s e-commerce search engine product called SNAP (Search, Navigation and Personalization). Prior to BloomReach, Vinodh Kumar worked at Google, where he was the man behind the Google News ranking algorithms, where he served as the Tech Lead of the Google News Quality team. Vinodh Kumar has a bachelors from Anna University and Masters in Computer Science from IISc. He was also the All India topper in GATE Computer science in 1999.

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All about data science and machine learning