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.
Machine Learning as a Service
You code, you test, you ship and you maintain
This workshop addresses one of the most common pain points we have come across with data scientists at many organizations : last-mile delivery of data science applications - moving data science solutions to production.
A lot of materials are available on how to do machine learning (including the authors of this workshop) - but hardly any cover how to put them in production and how to continue updating the model.
The attendees would learn how to build a seamless end-to-end data driven application - data ingestion, exploration, machine learning, RESTful API, dashboard, and making it repeatable - to solve a business prediction problem and present it to their clients.
“Jack of all trades, master of none, though oft times better than master of one”
One of the common pain points that we have come across in organizations is the last-mile delivery of data science applications. There are two common delivery vehicles of data products – dashboards and APIs.
More often than not, machine learning practitioners find it hard to deploy their work in production and full stack developers find it hard to incorporate machine learning models in their pipeline.
To be able to successfully do a data science-driven product/application, it requires one to have a basic understanding of machine learning, server-side programming and front-end application.
In this workshop, one would learn how to build a seamless end-to-end data driven application – Starting from data ingestion, data exploration, creating a simple machine learning model, exposing the output as a RESTful API and deploying the dashboard as a web application – to solve a business problem. The attendees would then learn how to make this process repeatable and automated - how to set up data pipelines and how to handle updates to data by updating models and/or dashboard.
We will be using Python stack for this workshop. The focus will be on breadth and getting a data-driven product completed by the end of the workshop.
- Data Engineering
- Data Ingestion from a database
- Data Exploration using pandas
- Machine Learning
- Build machine learning model using scikit-learn
- Creating dashboard using bokeh
- Deployment and API
- Creating RESTful API
- Integrating model output to DB
- Deployment on cloud (AWS)
- Automate Data Science Process
- Airflow/Luigi framework to build data pipelines
- Update model at a regular frequency (cron job)
- Discussions on Model tradeoffs during training and prediction
The repository for the workshop is here.
Learn how to build and deploy a machine learning application end-to-end.