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 firstname.lastname@example.org or call +91-7676332020.
Zero down time ML model swap using docker and kubernetes
At Gojek, we needed to improve the allocation of driver to customer. The behaviour of drivers across different regions are different. Models went stale depending on festivals and influx of new drivers to the system. Also a safe environment for the data science to play with the models was lacking.
We solved this problem by having multiple models running at different regions and different time periods with frequent changes without downtime. This session is about how we built a system using docker and kubernetes to handle multiple models and dynamically allotting traffic to multiple models based on the region or time.
This particular solution offered us the flexibility of running multiple models and redirecting traffic as deemed necessary. It also offered us the scalability if we needed one model to handle multiple regions.
A brief introduction to the topics covered. This section will introduce the business problems faced by Gojek in the domain of allocation of driver. The unique insight into the driver behaviour in Indonesia will also be spoken about in this section.
Problem statement(2-5 mins)
This section will restate the problem on technical grounds. It will also introduce other technical constraints that were important during the project inception phase.
A walkthrough through solution(20 mins)
This section will cover the technical solution that was implemented. It covers how we managed to deploy multiple models and scaled them through docker and kubernetes. It explains how we managed to dynamically allot traffic to multiple models with zero downtime. This section will also explain how monitoring and other process checks were in place to make sure that the model was working as expected. The usual deployment strategies around the models will also be explained here.
Why did we use this solution?(4-5 mins)
This section will cover why we went for this solution and how it impacted us.
The issues that we faced during the whole process will be explained here. Also things could have been improved will also be discussed here.
I am a Data Engineer at Gojek. I am part of the team that works on all things related to data. At Gojek, my work revolves around data pipelines, handling data at scale and building applications on top of data. I have also worked extensively in the Ruby world prior.