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

For more information about speaking proposals, tickets and sponsorships, contact or call +91-7676332020.

Hosted by

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

Vipul Mathur


Using data pipelines to navigate your data ocean

Submitted Apr 27, 2017

One of the main challenges facing companies adopting data-driven analytics-based approach to their business, is how to scale the development and adoption of data products throughout the company. In our experience, managed data pipelines is one approach that has emerged to address these challenges. This talk will introduce data pipelines, and illustrate how the challenges are addressed. The talk will be balanced with concepts, examples, and demos. Anyone interested in learning best practices to increase productivity and agility for analytics-based data products would benefit.

Intended audience

Architects/ data scientists/ engineers looking to scale their analytics work within a company.

Key takeaway: Data Pipelines

  • Are an emerging paradigm/ industry trend in development of data products
  • Address several challenges in developing/ scaling data products today
  • Provide a framework that enforces best practices for cloud-native apps
  • Have the potential to future-proof your data products
  • Are supported by most analytics frameworks, stacks, and cloud platforms today

Call to action

Let us know in the comments below what you would expect from this session.


Teaser: TL;DR [1min]

  • A TL;DR version of what this talk is all about

Motivation: WHY [8min]

  • Why do we need to evolve the way analytics solutions are built and deployed?
  • Illustration of challenges, with examples
    • Show what ad-hoc/ one-off solutions (without pipelines) look like
    • Dealing with multiple apps, multiple roles (DS/ DE/ BA/ ...)
    • Lifecycle of apps, leveraging/ reusing existing work
    • Parallels with scaling software development via collaboration and reuse (eg. GitHub)

Introduction to Data Pipelines: WHAT [10min]

  • What are Data Pipelines?
    • Establish pipeline concepts with examples
  • Data Pipelines available today (pointers only)
    • Cloud-based solutions: AWS, Azure, GCP
    • Analytics stacks: Hadoop, Spark
  • Data Pipelines vs Task Pipelines
    • Pipelines are not new, but data pipelines are special
  • Developing Data Products using Data Pipelines
    • Conceptual steps: devel/ deploy/ operate/ improve
    • Show how pipelines help with examples
  • Benefits of Data Products developed as Data Pipelines
    • Future proofing
    • Best practices

Managed Data Pipelines using CDAP: HOW [10mins]

  • Introduction to CDAP and Data Pipelines in CDAP
  • Demo of analytics app development and deployment in CDAP using Data Pipelines
  • Advantages of CDAP and Alternatives to CDAP

Wrap-up: NEXT [3min]

  • Apache Beam and the future of Data Pipelines (noteworthy future item)
  • Links and references (for audience to follow-up later)
  • Key take-home points (over Q&A)

Q&A [7min]

Speaker bio

7+ years of experience wading through petabytes of machine generated data to extract insights and business value using machine learning and other analytics techniques. Learnt the hard way how to make this work at scale within a large organization.


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Hosted by

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