by The Fifth Elephant

The Fifth Elephant 2018

The seventh edition of India's best data conference

The Fifth Elephant 2018

The Fifth Elephant 2018

The seventh edition of India's best data conference

by The Fifth Elephant
date_range

Date

26–27 Jul 2018, NIMHANS Convention Centre

place

Venue

NIMHANS Convention Centre

About

About the conference and topics for submitting talks:

The Fifth Elephant is rated as India’s best data conference. It is a conference for practitioners, by practitioners. In 2018, The Fifth Elephant will complete its seventh edition.

The Fifth Elephant is an evolving community of stakeholders invested in data in India. Our goal is to strengthen and grow this community by presenting talks, panels and Off The Record (OTR) sessions that present real insights about:

1. Data engineering and architecture: tools, frameworks, infrastructure, architecture, case studies and scaling.
2. Data science and machine learning: fundamentals, algorithms, streaming, tools, domain specific and data specific examples, case studies.
3. The journey and challenges in building data driven products: design, data insights, visualisation, culture, security, governance and case studies.
4. Talks around an emerging domain: such as IoT, finance, e-commerce, payments or data in government.

Target audience:

You should attend and speak at The Fifth Elephant if your work involves:

  1. Engineering and architecting data pipelines.
  2. Building ML models, pipelines and architectures.
  3. ML engineering.
  4. Analyzing data to build features for existing products.
  5. Using data to predict outcomes.
  6. Using data to create / model visualizations.
  7. Building products with data – either as product managers or as decision scientists.
  8. Researching concepts and deciding on algorithms for analyzing datasets.
  9. Mining data with greater speed and efficiency.
  10. Developer evangelists from organizations which want developers to use their APIs and technologies for machine learning, full stack engineering, and data science.

Perks for submitting proposals:

Submitting a proposal, especially with our process, is hard work. We appreciate your effort.
We offer one conference ticket at discounted price to each proposer, and a t-shirt.
We only accept one speaker per talk. This is non-negotiable. Workshops may have more than one instructor. In case of proposals where more than one person has been mentioned as collaborator, we offer the discounted ticket and t-shirt only to the person with who the editorial team corresponded directly during the evaluation process.

Format:

The Fifth Elephant is a two-day conference with two tracks on each day. Track details will be announced with a draft schedule in February 2018.

We are accepting sessions with the following formats:

  1. Full talks of 40 minutes.
  2. Crisp talks of 20 minutes.
  3. Off the Record (OTR) sessions on focussed topics / questions. An OTR is 60-90 minutes long and typically has up to four facilitators and one moderator.
  4. Workshops and tutorials of 3-6 hours duration on Machine Learning concepts and tools, full stack data engineering, and data science concepts and tools.
  5. Pre-events. Birds Of Feather (BOF) sessions, talks, and workshops for open houses and pre-events in Bangalore and other cities between October 2017 and June 2018.** Reach out to info@hasgeek.com should you be interested in speaking and/or hosting a community event between now and the conference in July 2018.

Selection criteria:

The first filter for a proposal is whether the technology or solution you are referring to is open source or not. The following criteria apply for closed source talks:

  1. If the technology or solution is proprietary, and you want to speak about your proprietary solution to make a pitch to the audience, you should pick up a sponsored session. This involves paying for the speaking slot. Write to fifthelephant.editorial@hasgeek.com
  2. If the technology or solution is in the process of being open sourced, we will consider the talk only if the solution is open sourced at least three months before the conference.
  3. If your solution is closed source, you should consider proposing a talk explaining why you built it in the first place; what options did you consider (business-wise and technology-wise) before making the decision to develop the solution; or, what is your specific use case that left you without existing options and necessitated creating the in-house solution.

The criteria for selecting proposals, in the order of importance, are:

  1. Key insight or takeaway: what can you share with participants that will help them in their work and in thinking about the ML, big data and data science problem space?
  2. Structure of the talk and flow of content: a detailed outline – either as mindmap or draft slides or textual description – will help us understand the focus of the talk, and the clarity of your thought process.
  3. Ability to communicate succinctly, and how you engage with the audience. You must submit link to a two-minute preview video explaining what your talk is about, and what is the key takeaway for the audience.

No one submits the perfect proposal in the first instance. We therefore encourage you to:

  1. Submit your proposal early so that we have more time to iterate if the proposal has potential.
  2. Talk to us on our community Slack channel: https://friends.hasgeek.com if you want to discuss an idea for your proposal, and need help / advice on how to structure it. Head over to the link to request an invite and join #fifthel.

Our editorial team helps potential speakers in honing their speaking skills, fine tuning and rehearsing content at least twice - before the main conference - and sharpening the focus of talks.

How to submit a proposal (and increase your chances of getting selected):

The following guidelines will help you in submitting a proposal:

  1. Focus on why, not how. Explain to participants why you made a business or engineering decision, or why you chose a particular approach to solving your problem.
  2. The journey is more important than the solution you may want to explain. We are interested in the journey, not the outcome alone. Share as much detail as possible about how you solved the problem. Glossing over details does not help participants grasp real insights.
  3. Focus on what participants from other domains can learn/abstract from your journey / solution. Refer to these talks from The Fifth Elephant 2017, which participants liked most: http://hsgk.in/2uvYKI9 and http://hsgk.in/2ufhbWb
  4. We do not accept how-to talks unless they demonstrate latest technology. If you are demonstrating new tech, show enough to motivate participants to explore the technology later. Refer to talks such as this: http://hsgk.in/2vDpag4 and http://hsgk.in/2varOqt to structure your proposal.
  5. Similarly, we don’t accept talks on topics that have already been covered in the previous editions. If you are unsure about whether your proposal falls in this category, drop an email to: fifthelephant.editorial@hasgeek.com
  6. Content that can be read off the internet does not interest us. Our participants are keen to listen to use cases and experience stories that will help them in their practice.

To summarize, we do not accept talks that gloss over details or try to deliver high-level knowledge without covering depth. Talks have to be backed with real insights and experiences for the content to be useful to participants.

Passes and honorarium for speakers:

We pay an honorarium of Rs. 3,000 to each speaker and workshop instructor at the end of their talk/workshop. Confirmed speakers and instructors also get a pass to the conference and networking dinner. We do not provide free passes for speakers’ colleagues and spouses.

Travel grants for outstation speakers:

Travel grants are available for international and domestic speakers. We evaluate each case on its merits, giving preference to women, people of non-binary gender, and Africans. If you require a grant, request it when you submit your proposal in the field where you add your location. The Fifth Elephant is funded through ticket purchases and sponsorships; travel grant budgets vary.

Last date for submitting proposals is: 31 March 2018.

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-recorded, 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 the solution you have built, or 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 a document showing the full workshop plan.

Contact details:

For more information about the conference, sponsorships, or any other information contact support@hasgeek.com or call 7676332020.

Call for proposals

None

Venue

Lakkasandra
Hombegowda Nagar
Bengaluru, Karnataka 560029, IN

All proposals

Confirmed sessions

Design for Data

Paul Meinshausen (@pmeins)

  • Full talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Thu, 12 Jul
  • slideshow

The power of intuition in data science, and why it will always have a role

Avi Patchava (@avipatch)

  • Full talk
  • Beginner
  • 3 upvotes
  • 3 comments
  • Wed, 4 Jul
  • slideshow

Compromising a $6B big data project through poor data quality: the Aadhaar case study

Anand Venkatanarayanan (@anandvenkatanarayanan)

  • Full talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Mon, 2 Jul
  • slideshow

GDPR- The wave of Data Privacy

Aina Rao (nagu rao) (@ainarao)

  • Intermediate
  • 2 upvotes
  • 0 comments
  • Sun, 1 Jul

The right to privacy versus the people's right to know: challenges and the way forward

Sushant Sinha (@sushant354)

  • Full talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Wed, 27 Jun
  • slideshow

Data science for business: adopting analytics without paralysis

Ajay Kelkar

  • Full talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Wed, 27 Jun

The battle for privacy: right to be forgotten in India

Jyoti Panday (@pandayjyoti)

  • Full talk
  • Beginner
  • 1 upvotes
  • 0 comments
  • Tue, 26 Jun
  • slideshow

Weaponizing data for politics

Shivam Shankar Singh (@shivamshankars)

  • Full talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Wed, 20 Jun
  • slideshow

Seeing through the eyes of a self-driving car: visualizing autonomous vehicle data on the web

Xiaoji Chen (@xiaoji)

  • Full talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Wed, 13 Jun

Michelangelo: Uber's machine learning platform

Achal Shah (@achals)

  • Full talk
  • Intermediate
  • 3 upvotes
  • 3 comments
  • Wed, 13 Jun

So you think you know about linear regression ...

Chris Stucchio (@stucchio)

  • Full talk
  • Beginner
  • 3 upvotes
  • 0 comments
  • Mon, 11 Jun

A study in classification

Ramanan Balakrishnan (@ramananbalakrishnan)

  • Crisp talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Tue, 29 May
  • slideshow

Operating data pipeline using Airflow @ Slack

Ananth Durai (@vananth22)

  • Full talk
  • Advanced
  • 1 upvotes
  • 0 comments
  • Wed, 9 May
  • play_arrow
  • slideshow

Needle in a haystack : entity search on text and graph

Uma Sawant (@umasawant)

  • Full talk
  • Beginner
  • 2 upvotes
  • 0 comments
  • Tue, 1 May
  • slideshow

Deep learning based hybrid recommendation systems in TensorFlow

Vijay Srinivas Agneeswaran, Ph.D (@vijayagneeswaran)

  • Workshop
  • Intermediate
  • 4 upvotes
  • 1 comments
  • Wed, 25 Apr
  • slideshow

Using structural estimation methods from economics to model user behaviour in bike-sharing systems

Ashish Kabra (@akabra)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 3 comments
  • Wed, 11 Apr
  • play_arrow
  • slideshow

Our experiments with food recommendations @Swiggy

nitin hardeniya (@noting)

  • Crisp talk
  • Intermediate
  • 4 upvotes
  • 1 comments
  • Sat, 31 Mar
  • slideshow

Serviceability under high demand

Venkateshan K (@venky81)

  • Full talk
  • Intermediate
  • 1 upvotes
  • 1 comments
  • Sat, 31 Mar

User response prediction at scale

Priyanka Bhatt (@priyanka-bhatt)

  • Full talk
  • Intermediate
  • 8 upvotes
  • 2 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Improving product discovery via relevance and ranking optimization

Akash Khandelwal (@akash099)

  • Full talk
  • Intermediate
  • 4 upvotes
  • 2 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Scaling write-heavy OLTP systems with strong data guarantees: learning from Flipkart’s user facing order capture systems

Gokulvanan V Velan (Customer Platform - VS)

  • Full talk
  • Intermediate
  • 48 upvotes
  • 6 comments
  • Sat, 31 Mar
  • slideshow

Segmenting 500 million users using Airflow + Hive

Soumya Shukla (@soumyashukla22)

  • Crisp talk
  • Intermediate
  • 4 upvotes
  • 5 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Building analytics application with streaming expressions in Apache Solr

Amrit Sarkar (@sarkaramrit2)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 2 comments
  • Wed, 28 Mar
  • play_arrow
  • slideshow

Building a next generation speech and NLU engine: in pursuit of multi-modal experience for Bixby

Vikram Vij (@vikramvij)

  • Crisp talk
  • Intermediate
  • 12 upvotes
  • 8 comments
  • Wed, 28 Mar
  • play_arrow
  • slideshow

Market propensity modelling using XStream: unified self-service analytics ETL and ML platform

Puneet (@puneetkrojha)

  • Sponsored talk
  • Advanced
  • 28 upvotes
  • 0 comments
  • Tue, 27 Mar
  • slideshow

Deep portfolio: using neural networks for portfolio construction

Anant Gupta (@anantguptadbl)

  • Full talk
  • Intermediate
  • 14 upvotes
  • 8 comments
  • Tue, 27 Mar
  • slideshow

Qubole Sparklens: understanding the scalability limits of Spark applications

Rohit Karlupia (@sixthelephant)

  • Full talk
  • Intermediate
  • 8 upvotes
  • 0 comments
  • Mon, 26 Mar
  • play_arrow
  • slideshow

Incremental transform of transactional data models to analytical data models in near real time

Govind Pandey (@govind-pandey)

  • Full talk
  • Intermediate
  • 14 upvotes
  • 2 comments
  • Mon, 26 Mar
  • play_arrow
  • slideshow

Improve data quality using Apache Airflow and check operator

Sakshi Bansal (@sakshi28)

  • Crisp talk
  • Intermediate
  • 25 upvotes
  • 4 comments
  • Wed, 21 Mar
  • play_arrow
  • slideshow

Scalability truths and serverless architectures: why it is harder with stateful, data-driven systems

Regunath B

  • Full talk
  • Intermediate
  • 9 upvotes
  • 0 comments
  • Wed, 14 Mar
  • slideshow

Atlas: GO-JEK’s real-time geospatial visualization platform

Ravi Suhag (@ravisuhag)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 3 comments
  • Tue, 20 Feb
  • slideshow

Math for data science

Vishal (@vishalgokhale)

  • Workshop
  • Beginner
  • 0 upvotes
  • 0 comments
  • Tue, 30 Jan
  • slideshow

Unconfirmed proposals

DIY - Data is Yours

Gaurav Singhania (@gauravsinghania)

  • Intermediate
  • 1 upvotes
  • 0 comments
  • Mon, 25 Jun

Route risks using driving data on road segments

Jayanta Pal (@jayantapal)

  • Intermediate
  • 1 upvotes
  • 1 comments
  • Wed, 20 Jun

Technology to counter misinformation/disinformation

Pratik Sinha (@pratiksinha)

  • Crisp talk
  • Intermediate
  • 2 upvotes
  • 0 comments
  • Sun, 17 Jun

NLP on भारतीय भाषाओं

Hitesh Mantrala (@hitman-hittudiv)

  • Crisp talk
  • Intermediate
  • 3 upvotes
  • 0 comments
  • Wed, 23 May
  • slideshow

An Introduction to Interactive Data Visualization with Bokeh

Uddipta Bhattacharjee (@uddiptab)

  • Crisp talk
  • Beginner
  • 0 upvotes
  • 0 comments
  • Sat, 31 Mar

Managing Machine Learning Models in Production

Anand Chitipothu (@anandology)

  • Crisp talk
  • Intermediate
  • 3 upvotes
  • 2 comments
  • Sat, 31 Mar
  • slideshow

DevOps for Data Science: Experiences from building a cloud-based data science platform

Anand Chitipothu (@anandology)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 3 comments
  • Sat, 31 Mar
  • slideshow

Display prospecting using explore-exploit strategy

Akshita Sukhlecha (@akshita-sukhlecha)

  • Crisp talk
  • Intermediate
  • 7 upvotes
  • 1 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

A Time Series Analysis of District-wise Government Spending

Gaurav Godhwani (@gggodhwani)

  • Full talk
  • Beginner
  • 7 upvotes
  • 2 comments
  • Sat, 31 Mar
  • slideshow

Personalized Recommendations for Computational Advertising

Surabhi Punjabi (@surabhi-punjabi)

  • Full talk
  • Intermediate
  • 6 upvotes
  • 0 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Building Streaming platform using Kafka Streams

ADDEPALLI GIRIDHAR (@connect2ppl)

  • Crisp talk
  • Intermediate
  • 7 upvotes
  • 1 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Expressing complex ETL pipelines using Cascading

Neha Kumari (@neha-kumari)

  • Crisp talk
  • Beginner
  • 13 upvotes
  • 0 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Building big data pipelines on kafka and kubernetes

Abhishek Agarwal (@abhishek-appd)

  • Full talk
  • Intermediate
  • 22 upvotes
  • 3 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Approximate Query Processing

DEEPAK GOYAL (@zonker)

  • Crisp talk
  • Beginner
  • 6 upvotes
  • 1 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Applying Lambda Architecture in Machine Learning realm

Akash Khandelwal (@akash099)

  • Full talk
  • Intermediate
  • 2 upvotes
  • 3 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Using Operations Research and Analytics to Propel the E-commerce Industry

Dr Amit Garg (@garg78)

  • Crisp talk
  • Intermediate
  • 9 upvotes
  • 2 comments
  • Sat, 31 Mar
  • play_arrow
  • slideshow

Machine Learning using Orange - It's Fruitful and Fun!

Ankit Mahato (@ankitmahato)

  • Workshop
  • Beginner
  • 2 upvotes
  • 0 comments
  • Fri, 30 Mar
  • play_arrow
  • slideshow

Driving Customer Service Optimization using supervised stack ensemble with natural language features

Tredence Inc (proposing)

  • Full talk
  • Intermediate
  • 19 upvotes
  • 4 comments
  • Fri, 30 Mar
  • play_arrow
  • slideshow

Scaling up our distributed query workloads using Kafka Streams + Rocks DB

Ronakkumar Kothari (@ronakkothari)

  • Full talk
  • Intermediate
  • 29 upvotes
  • 6 comments
  • Fri, 30 Mar
  • play_arrow
  • slideshow

Smart Campaign Planning Through "Intelligent" Email Outreach Using NLG

Tredence Inc (proposing)

  • Full talk
  • Intermediate
  • 20 upvotes
  • 5 comments
  • Fri, 30 Mar
  • play_arrow
  • slideshow

Building Scalable Machine Learning pipelines with Apache Prediction IO

Rajdeep Dua (@rajdeepd)

  • Full talk
  • Intermediate
  • 1 upvotes
  • 0 comments
  • Fri, 30 Mar
  • slideshow

Bad Data is No Better Than No Data! - Impact of Automation in Data Stewardship Workflows in Plant Agriculture Industry

Karnam Vasudeva Rao (@vasukarnam)

  • Crisp talk
  • Intermediate
  • 14 upvotes
  • 3 comments
  • Fri, 30 Mar
  • play_arrow
  • slideshow

Baking a cloud-native data warehouse from enterprise database leftovers

Vineeti Louis (@vineetilouis)

  • Crisp talk
  • Intermediate
  • 12 upvotes
  • 0 comments
  • Tue, 27 Mar
  • play_arrow
  • slideshow

Distributed Deep Learning

Somya Kumar (@somyak)

  • Crisp talk
  • Intermediate
  • 16 upvotes
  • 4 comments
  • Mon, 26 Mar
  • play_arrow
  • slideshow

What I learnt by running Apache Airflow @Scale

Sreenath S Kamath (@sree92)

  • Crisp talk
  • Intermediate
  • 7 upvotes
  • 1 comments
  • Mon, 26 Mar

Building microservices using kafka

anugrah nayar (@codewalker)

  • Crisp talk
  • Beginner
  • 7 upvotes
  • 2 comments
  • Fri, 23 Mar
  • play_arrow
  • slideshow

Using Data to make data processing reliable again

devjyoti (@kprotocol)

  • Full talk
  • Intermediate
  • 16 upvotes
  • 2 comments
  • Wed, 21 Mar

Hybrid Machine Learning with Azure IoT Edge

SUNIL KUMAR (@sunilkumarjb)

  • Workshop
  • Intermediate
  • 4 upvotes
  • 2 comments
  • Tue, 20 Mar

Business analytics on the cloud - a scalable model with R

Praveen Chandrasekharan (@pchandra)

  • Crisp talk
  • Intermediate
  • 30 upvotes
  • 4 comments
  • Sun, 18 Mar
  • slideshow

Banker to the unbanked- story of scale leveraging Data Science, AWS, Scala, Spark

Arpit Gupta (@callarpit)

  • Full talk
  • Beginner
  • 1 upvotes
  • 3 comments
  • Fri, 16 Mar
  • play_arrow
  • slideshow

Beyond Data stores & processing engines - Learnings from handling eCommerce Data in motion

Regunath B

  • Full talk
  • Intermediate
  • 3 upvotes
  • 0 comments
  • Thu, 15 Mar

Big Data Forensic Analytics

Deepak Mane (@deepak5278)

  • Full talk
  • Beginner
  • 1 upvotes
  • 3 comments
  • Sun, 4 Mar
  • slideshow

Complex network analysis using NetworkX - Graph Theory in Python

Himanshu Mishra (@orkohunter-himanshu)

  • Workshop
  • Beginner
  • 3 upvotes
  • 1 comments
  • Thu, 1 Feb
  • slideshow

Deep Learning for NLP from scratch

Nishant Nikhil (@nishnik)

  • Workshop
  • Intermediate
  • 2 upvotes
  • 0 comments
  • Wed, 31 Jan

Robot Quotient - The Machine versus Human Debate

Puneet Mathur (@puneetmathur)

  • Crisp talk
  • Advanced
  • 11 upvotes
  • 19 comments
  • Fri, 26 Jan
  • slideshow

Machine Learning for Financial Data Extraction

Karthik Gali (@karthikgali)

  • Crisp talk
  • Beginner
  • 2 upvotes
  • 2 comments
  • Wed, 13 Dec
  • play_arrow
  • slideshow

Topological Data Analysis Theory and Practice

Milan Joshi (@mlnjsh)

  • Full talk
  • Intermediate
  • 3 upvotes
  • 16 comments
  • Thu, 17 Aug