The Fifth Elephant

The Fifth Elephant 2017

On data engineering and application of ML in diverse domains

##Theme and format
The Fifth Elephant 2017 is a four-track conference on:

  1. Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
  2. Application of Machine Learning (ML) in diverse domains such as IOT, payments, e-commerce, education, ecology, government, agriculture, computational biology, social network analysis and emerging markets.
  3. Hands-on tutorials on data mining tools, and ML platforms and techniques.
  4. Off-the-record (OTR) sessions on privacy issues concerning data; building data pipelines; failure stories in ML; interesting problems to solve with data science; and other relevant topics.

The Fifth Elephant is a conference for practitioners, by practitioners.

Talk submissions are now closed.

You must submit the following details along with your proposal, or within 10 days of submission:

  1. Draft slides, mind map or a textual description detailing the structure and content of your talk.
  2. Link to a self-record, two-minute preview video, where you explain what your talk is about, and the key takeaways for participants. This preview video helps conference editors understand the lucidity of your thoughts and how invested you are in presenting insights beyond your use case. Please note that the preview video should be submitted irrespective of whether you have spoken at past editions of The Fifth Elephant.
  3. If you submit a workshop proposal, you must specify the target audience for your workshop; duration; number of participants you can accommodate; pre-requisites for the workshop; link to GitHub repositories and documents showing the full workshop plan.

##About the conference
This year is the sixth edition of The Fifth Elephant. The conference is a renowned gathering of data scientists, programmers, analysts, researchers, and technologists working in the areas of data mining, analytics, machine learning and deep learning from different domains.

We invite proposals for the following sessions, with a clear focus on the big picture and insights that participants can apply in their work:

  • Full-length, 40-minute talks.
  • Crisp, 15-minute talks.
  • Sponsored sessions, of 15 minutes and 40 minutes duration (limited slots available; subject to editorial scrutiny and approval).
  • Hands-on tutorials and workshop sessions of 3-hour and 6-hour duration where participants follow instructors on their laptops.
  • Off-the-record (OTR) sessions of 60-90 minutes duration.

##Selection Process

  1. Proposals will be filtered and shortlisted by an Editorial Panel.
  2. Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
  3. Proposers are also encouraged to vote and comment on other proposals submitted here.

Selection Process Flowchart

We will notify you if we move your proposal to the next round or reject it. A speaker is NOT confirmed for a slot unless we explicitly mention so in an email or over any other medium of communication.

Selected speakers must participate in one or two rounds of rehearsals before the conference. This is mandatory and helps you to prepare well for the conference.

There is only one speaker per session. Entry is free for selected speakers.

##Travel grants
Partial or full grants, covering travel and accomodation are made available to speakers delivering full sessions (40 minutes) and workshops. Grants are limited, and are given in the order of preference to students, women, persons of non-binary genders, and speakers from Asia and Africa.

##Commitment to Open Source
We believe in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like for it to be available under a permissive open source licence. If your software is commercially licensed or available under a combination of commercial and restrictive open source licences (such as the various forms of the GPL), you should consider picking up a sponsorship. We recognise that there are valid reasons for commercial licensing, but ask that you support the conference in return for giving you an audience. Your session will be marked on the schedule as a “sponsored session”.

##Important Dates:

  • Deadline for submitting proposals: June 10
  • First draft of the coference schedule: June 20
  • Tutorial and workshop announcements: June 20
  • Final conference schedule: July 5
  • Conference dates: 27-28 July

##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.

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The Fifth Elephant
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The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
All submissions

Danish M

@pixelgenie

How to build scalable and robust data pipeline iteratively.

Submitted Jun 4, 2017

Section: Full talk for data engineering track Technical level: Intermediate

I will drill down to understand how startups can build scalable data pipeline using open source tools. What do all these tools do and how do they fit into the ecosystem? And how to iteratively build a scalable and robust data engineering pipeline as you grow as a company ?

Outline #

Companies, non-profit organizations, and governments are all starting to realize the huge value that data can provide to customers, decision makers, and concerned citizens. What is often neglected is the amount of engineering required to make that data accessible. Simply using SQL is no longer an option for large, unstructured, or real-time data. Building a system that makes data usable becomes a monumental challenge for data engineers.

There is no plug and play solution that solves every use case. A data pipeline meant for serving ads will look very different from a data pipeline meant for retail analytics. Since there are unlimited permutations of open-source technologies that can be cobbled together, it can be overwhelming when you first encounter them.

I will drill down to understand how startups can build scalable data pipeline using open source tools. What do all these tools do and how do they fit into the ecosystem? And how to iteratively build a scalable and robust data engineering pipeline as you grow as a company ?

Requirements #

NA

Speaker bio #

Co-founder & Growth Marketer at PixelGenie. Former Insight Data Science Fellow 2016, NYC.

Started as a self-taught product geek. Worked and helped many startups to build their analytics infrastructure from grounds up. Amalgamation of tech + marketing is what i am most interested about. Believes, product is not just the technology. Its is the whole experience around an offering - from Tech to Marketing to sales and post-sales experience.

I read/write about Growth & Data Science. I believe, Internet is the most democratic system ever created. A person sitting in his or her bedroom with willingness, a laptop and a good internet connection can change this world for better. This is possible in the age of Internet.

All submissions

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Hosted by

The Fifth Elephant
The Fifth Elephant
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more