The Fifth Elephant 2017
On data engineering and application of ML in diverse domains
Jul 2017
24 Mon
25 Tue
26 Wed
27 Thu 08:15 AM – 10:00 PM IST
28 Fri 08:15 AM – 06:25 PM IST
29 Sat
30 Sun
##Theme and format
The Fifth Elephant 2017 is a four-track conference on:
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:
##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:
##Selection Process
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:
##Contact
For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.
Hosted by
Rajaram Mallya
@rajarammallya
Submitted Jun 10, 2017
In the new world of microservices, every service lives independently with its own databases. But then, they still need data from other microservices to function. It becomes harder and harder for running any kind of analytics or data science on all this fragmented data.
In this democratic, decentralized world how do you empower microservices teams to build their own data pipelines? How do you enable numerous small teams to aggregate and transform real time streams of data? And what about frequently changing schemas of all the other microservices a team uses? How do we make this possible without having evey team learn the intricate nuances of today’s real time streaming pipelines?
This session addresses how we tackled all these problems by building the right data platform tools in Gojek. How we used Kafka, Protobuf schemas and Flink aggregations to democratise data for other engineering teams within the org. How we abstracted out the nuances and comman pitfalls of data pipelines and allowed microservice teams to concentrate on their own business logic. And how we provided a safe, decentralized environment within Gojek for all teams - from product to data science - to experiment, transform and aggregate data in any form or fashion.
At the end of this session, I hope you will take away some of the lessons we learned on how to decentralize data access, how to empower other engineering teams to consume data and how to provide them the support they need for DIY data pipelines.
I am a data geek at Gojek, Indonesia’s largest Unicorn. I work in the core data engineering team. My previous stints include an AI startup and a long stint at Thoughtworks.
Hosted by
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