Theme and format
The Fifth Elephant 2017 is a four-track conference on:
- Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
- 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.
- Hands-on tutorials on data mining tools, and ML platforms and techniques.
- 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:
- Draft slides, mind map or a textual description detailing the structure and content of your talk.
- 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.
- 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.
- Proposals will be filtered and shortlisted by an Editorial Panel.
- Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
- Proposers are also encouraged to vote and comment on other proposals submitted here.
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.
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”.
- 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 email@example.com or call +91-7676332020.
Building camera based intelligent applications
Camera based intelligent applications are lot of fun! There are many practical applications of it like Industrial Counters, Real Time Object Tracking, Object Classification, Road Traffic Estimation etc. While they are fun and interesting, building them is not that trivial. Generally, building camera based intelligent applications require many modules in the pipelines and a data scientist may not be aware of those. It involves managing hardware, machine learning, a dashboard for user interaction and data visualization and most importantly a way to glue them all together. Taking care of all these requires a considerable amount of time to deploy an application.
I have been working on trying to build tools to simplify and streamline this process and allow data scientists to build such an application in days instead of weeks. In this talk, I will discuss about the approach we have taken, the tools that we have built, of which some are open source, and explain how these tools improved the overall time needed to build camera based intelligent applications.
The talk will be structured into 3 parts - 8-10mins for the presentation, 3-5mins for the hands-on demo to building an application from scratch and the rest for questions.
The presentation is structured as follows with details in slide link provided:
- About me
- Modules in a camera based application
a. Data Ingestion
c. Machine Learning
- Our approach
a. User agent on data capture device
b. A tool to deploy ML functions - Firefly
c. Modular dashboard components
In the hands on demo part, I will go through building one image/video based machine learning problem that a data scientist may want to make using our open source modules. I will show how to deploy the application on the rorocloud platform or on your own servers or local machine.
Basic knowledge of building blocks of a machine learning application. None in terms of hardware.
The speaker is Nabarun Pal, an undergraduate student at Indian Institute of Technology Roorkee who just finished his pre-final year. Currently, he is working for rorodata which aims at providing data scientists a platform to build and deploy their models without the need of worrying about infrastructure, scalability and performance.
He is passionate about software development. He can also talk about Internet of Things, Electronics, Robotics with equal spirit. His journey with the field of software and robotics started in his schooling days. He represents the college in various Robotics competitions and was involved in projects related to the above domains, brief of which can be found here