##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.
Recommendation Engine for Wide Transactions
Many applications we use today are powered by cloud and mobile. One of the critical components that drives engagement for the platforms on cloud is the recommendation engine. Recommendation systems are becoming all-pervasive. The transactions/interactions we have with the platform decide the next set of recommended items. As both users and the number of products offered on the platform scale, we are hit with two kinds of challenges - engineering and machine learning.
This talk is about how we designed a real-time recommendation engine at Red Hat when the transactions data is both Big Data and Wide data. We define wide data as those whose number of items in a transaction basket is greater than 1000. Some examples of big and wide data are: Financial Instruments traded by portfolio manager in a day; Products shipped from a warehouse; software components in a cloud platform etc.
The standard approaches have been market basket analysis(frequent pattern mining), collaborative filtering(matrix factorization) and currently deep learning.
Apache Spark lends itself nicely to build the data science pipeline: ingestion, data processing and machine learning. Out of the box, we have a parallel implementation of FP Growth algorithm for mining frequent itemsets. But as our data became wider, model training performance took a hit. This talk discusses how we used another popular recommendation algorithm in Spark - Alternating Least Squares to generate frequent itemsets. The new approach was faster and scaled well for big and wide data.
The engineering and data science approaches are novel and the attendees will learn how to build recommendation systems on the cloud, some of the challenges and some ideas on how to overcome them.
Who is this presentation for:
Data Scientists, Product Managers
Attendees will learn how to build recommendation systems for wide data. Attendees will also learn how to use Apache Spark to build ML pipeline for recommendation system.
Interest in data science. It will help if attendees know some examples of recommendation systems.
- Introduction to the problem (recommendations for big and wide data)
- Overview of common approaches (Frequent Pattern mining)
- Implementation of a common approach on Apache Spark (FP Growth)
- Challenges with traditional models for recommendation systems
- How we tested/measured the bottlenecks
- Other approach considerations: Alternate Least Squares, Deep Learning
- Pros and Cons of those approaches
- Use Apache Spark ALS to generate frequent patterns
- Performance comparisons - model training and in real-time predictions
- The chosen model architecture
- Demo and closing thoughts
Harjinder Mistry is currently a member of Developer-Tools team in RedHat, where he is incorporating data science into next-generation developer tools powered by Spark. Prior to RedHat, he was a member of IBM Analytics team and he developed Spark-ML pipeline components of IBM Analytics Platform. Earlier, he had spent several years in DB2 SQL Query Optimizer team building and fixing the mathematical model that decides the query execution plan. He holds M.Tech. degree from IIIT, Bangalore, India.