by The Fifth Elephant

The Fifth Elephant 2017

On data engineering and application of ML in diverse domains

The Fifth Elephant 2017

On data engineering and application of ML in diverse domains

by The Fifth Elephant
date_range

Date

27–28 Jul 2017, Bangalore

place

Venue

MLR Convention Centre, Whitefield

About

Theme and format

The Fifth Elephant 2017 is a four-track conference on:

  1. Data engineering – building pipelines and platforms; exposure to latest open source tools for data mining and real-time analytics.
  2. 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.
  3. Hands-on tutorials on data mining tools, and ML platforms and techniques.
  4. 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:

  1. Draft slides, mind map or a textual description detailing the structure and content of your talk.
  2. 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.
  3. 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.

Selection Process

  1. Proposals will be filtered and shortlisted by an Editorial Panel.
  2. Proposers, editors and community members must respond to comments as openly as possible so that the selection processs is transparent.
  3. Proposers are also encouraged to vote and comment on other proposals submitted here.

Selection Process Flowchart

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:

  • 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

Contact

For more information about speaking proposals, tickets and sponsorships, contact info@hasgeek.com or call +91-7676332020.


Venue

Dyvasandra Industrial Layout Mahadevapura,
Whitefield,
Bengaluru, , Karnataka 560048, IN

All proposals

Confirmed sessions

Bits and joules: data-driven energy systems

Deva P. Seetharam (@dpseetharam)

  • Full talk for Data in Government track
  • Beginner
  • 1 upvotes
  • 0 comments
  • Tue, 25 Jul

How We Built Our Machine Intelligence To Help Humans Save Lives

Zainul Charbiwala (@zainulcharbiwala)

  • Full talk for Data in Government track
  • Beginner
  • 1 upvotes
  • 0 comments
  • Sat, 22 Jul

Open data in government: challenges, and the case of Telangana Open Data Initiative

Rakesh Dubbudu (@rakeshdubbudu)

  • Full talk for Data in Government track
  • Beginner
  • 1 upvotes
  • 0 comments
  • Wed, 12 Jul
  • slideshow

Time Processing and Watermarks using Google Pub\Sub and Google DataFlow.

Swapnil Dubey (@swapnildubey)

  • Pune Meetup
  • Advanced
  • 5 upvotes
  • 0 comments
  • Thu, 29 Jun

Finding topics in short texts

Yash Gandhi (@yashgandhi)

  • Pune Meetup
  • Intermediate
  • 17 upvotes
  • 1 comments
  • Wed, 28 Jun

Introduction to recommendation systems with Python

Harshad Saykhedkar (@harshss)

  • Pune Meetup
  • Intermediate
  • 13 upvotes
  • 0 comments
  • Thu, 22 Jun

Interactive Data Visualisation using Markdown

Amit Kapoor (@amitkaps)

  • Full talk for data engineering track
  • Beginner
  • 3 upvotes
  • 0 comments
  • Mon, 12 Jun
  • play_arrow
  • slideshow

Maps ❤️ Data: A voyage across the world of geo-visualization

Rasagy Sharma (@rasagy)

  • Full talk for data engineering track
  • Intermediate
  • 4 upvotes
  • 0 comments
  • Sat, 10 Jun
  • slideshow

Distributed Machine Learning - Challenges and Oppurtunities

Anand Chitipothu (@anandology)

  • Crisp talk for data engineering track
  • Intermediate
  • 7 upvotes
  • 0 comments
  • Sat, 10 Jun
  • play_arrow
  • slideshow

Augmenting Solr’s NLP Capabilities with Deep-Learning Features to Match Images

Kumar Shubham (@kumar-shubham)

  • Crisp talk for data engineering track
  • Intermediate
  • 10 upvotes
  • 2 comments
  • Fri, 9 Jun
  • play_arrow
  • slideshow

Near Real time indexing/search in E-commerce marketplace : Approaches and Learnings

Umesh Prasad (@umeshprasad)

  • Full talk for data engineering track
  • Intermediate
  • 8 upvotes
  • 1 comments
  • Fri, 9 Jun
  • play_arrow
  • slideshow

Interactive Realtime Dashboards on Data Streams using Kafka, Druid and Superset

Nishant Bangarwa (@nishantbangarwa)

  • Full talk for data engineering track
  • Intermediate
  • 5 upvotes
  • 1 comments
  • Tue, 6 Jun
  • slideshow

Machine Learning as a Service

Bargava Subramanian (@barsubra)

  • Workshops
  • Beginner
  • 4 upvotes
  • 1 comments
  • Mon, 29 May

Apache Atlas Introduction: Need for Governance and Metadata management

Vimal Sharma (@svimal2106)

  • Full talk for data engineering track
  • Intermediate
  • 2 upvotes
  • 1 comments
  • Fri, 26 May
  • slideshow

Lessons learned from building a globally distributed database service from the ground up

Dharma Shukla (@dharmashukla)

  • Full talk for data engineering track
  • Intermediate
  • 25 upvotes
  • 8 comments
  • Fri, 26 May
  • play_arrow
  • slideshow

What explains our marks?

Anand S (@sanand0)

  • Crisp talk for Data in Government track
  • Beginner
  • 8 upvotes
  • 3 comments
  • Wed, 24 May
  • slideshow

Do you know what's on TV?

Bharath Mohan (@bharathmohan)

  • Full talk for data engineering track
  • Intermediate
  • 6 upvotes
  • 6 comments
  • Mon, 22 May
  • play_arrow
  • slideshow

Developing and Deploying Analytics for Internet of Things (IoT)

Amit Doshi (@amitdoshi)

  • Sponsored session
  • Intermediate
  • 7 upvotes
  • 7 comments
  • Mon, 22 May
  • slideshow

What database? - a practical guide to selection from NoSQL, SQL and Polyglot data stores

Regunath Balasubramanian (@regunathb)

  • Full talk for data engineering track
  • Intermediate
  • 2 upvotes
  • 2 comments
  • Mon, 22 May
  • play_arrow
  • slideshow

Plumbing data science pipelines

Krishnapriya Satagopan (@kpsatagopan)

  • Crisp talk for data engineering track
  • Intermediate
  • 11 upvotes
  • 2 comments
  • Mon, 22 May
  • play_arrow
  • slideshow

Wait, I can explain this! (ML models explaining their predictions)

Ramprakash R (@ramprakashr)

  • Crisp talk for data engineering track
  • Intermediate
  • 19 upvotes
  • 4 comments
  • Mon, 22 May
  • play_arrow
  • slideshow

Gabbar: Machine learning to guard OpenStreetMap

Bhargav Kowshik (@bkowshik)

  • Full talk for data engineering track
  • Intermediate
  • 5 upvotes
  • 5 comments
  • Sun, 30 Apr
  • play_arrow
  • slideshow

Adapting Bandit Algorithms to optimise user experience at Practo Consult

Santosh GSK (@santoshgadde)

  • Crisp talk for data engineering track
  • Intermediate
  • 30 upvotes
  • 4 comments
  • Sun, 30 Apr
  • play_arrow
  • slideshow

How we are building serverless architectures for Deep Learning & NLP at Episource

Manas Ranjan Kar (@manasrkar-episource)

  • Crisp talk for data engineering track
  • Intermediate
  • 7 upvotes
  • 2 comments
  • Sun, 30 Apr
  • play_arrow
  • slideshow

Transforming India's Budgets into Open Linked Data

Gaurav Godhwani (@gggodhwani)

  • Full talk for Data in Government track
  • Intermediate
  • 10 upvotes
  • 2 comments
  • Sun, 30 Apr
  • play_arrow
  • slideshow

5 Lessons I’ve Learned Tackling Product Matching for E-commerce

Govind Chandrasekhar (@gc20)

  • Full talk for data engineering track
  • Intermediate
  • 4 upvotes
  • 2 comments
  • Sat, 29 Apr
  • slideshow

Designing Machine Learning Pipelines for Mining Transactional SMS Messages

Paul Meinshausen (@pmeins)

  • Full talk for data engineering track
  • Intermediate
  • 12 upvotes
  • 2 comments
  • Fri, 28 Apr
  • play_arrow
  • slideshow

Fraud Detection & Risk Management in Payment Systems implemented using a Hybrid Memory Database

Srini V. Srinivasan (@drvsrinivasan)

  • Full talk in Payment Analytics track
  • Intermediate
  • 22 upvotes
  • 0 comments
  • Thu, 27 Apr
  • play_arrow
  • slideshow

Suuchi - Toolkit to build distributed systems

Sriram R (@brewkode)

  • Full talk for data engineering track
  • Intermediate
  • 26 upvotes
  • 9 comments
  • Wed, 26 Apr
  • play_arrow
  • slideshow

Machine Learning from Practice to Production

Ramanan Balakrishnan (@ramananbalakrishnan)

  • Full talk for data engineering track
  • Beginner
  • 13 upvotes
  • 2 comments
  • Tue, 25 Apr
  • play_arrow
  • slideshow

Distributed Consensus and Data Safety: NewSQL Perspective

Vijay Srinivas Agneeswaran, Ph.D (@vijayagneeswaran)

  • Full talk for data engineering track
  • Intermediate
  • 8 upvotes
  • 5 comments
  • Tue, 18 Apr
  • play_arrow
  • slideshow

From a recommendations carousel to personalizing entire app - personalization story at paytm

Charumitra Pujari (@charupujari)

  • Full talk in Payment Analytics track
  • Advanced
  • 10 upvotes
  • 1 comments
  • Tue, 4 Apr
  • slideshow

Unconfirmed proposals

Building a converged platform for data analytics

David Sangma (@davidsangma) (proposing)

  • Crisp talk for data engineering track
  • Advanced
  • 0 upvotes
  • 1 comments
  • Mon, 12 Jun

Democratising Data in the Microservices World

Rajaram Mallya (@rajarammallya)

  • Full talk for data engineering track
  • Intermediate
  • 4 upvotes
  • 1 comments
  • Sat, 10 Jun

Gen Z BI Paradigm - A Scalable , hybrid and collaborative Visualization Architecture using Spark , No SQL and Restful API

Deepikavalli A (@deepikavalli)

  • Crisp talk for data engineering track
  • Intermediate
  • 1 upvotes
  • 2 comments
  • Sat, 10 Jun
  • play_arrow
  • slideshow

Zero down time ML model swap using docker and kubernetes

anugrah nayar (@codewalker)

  • Full talk for data engineering track
  • Beginner
  • 8 upvotes
  • 0 comments
  • Sat, 10 Jun
  • play_arrow
  • slideshow

Multi-channel conversational chatbot platform powered by NLP engine

Prakash Mall (@prakashmall)

  • Crisp talk for data engineering track
  • Beginner
  • 1 upvotes
  • 1 comments
  • Sat, 10 Jun

Building camera based intelligent applications

Nabarun Pal (@palnabarun)

  • Crisp talk for data engineering track
  • Intermediate
  • 10 upvotes
  • 0 comments
  • Sat, 10 Jun
  • play_arrow
  • slideshow

Making sense of Digital and Physical Documents using ML and Optical Character Recognition

Nitin Saraswat (@chunky)

  • Full talk for data engineering track
  • Intermediate
  • 17 upvotes
  • 0 comments
  • Sat, 10 Jun
  • play_arrow
  • slideshow

ML Goes Fruitful

Preeti Negi (@preeti14dec)

  • Workshops
  • Beginner
  • 1 upvotes
  • 0 comments
  • Sat, 10 Jun
  • play_arrow
  • slideshow

How Machine Learning Algorithms evolved at Haptik while it's Chatbot catered to 200 million messages

krupal Modi (@superkrups)

  • Full talk for data engineering track
  • Intermediate
  • 26 upvotes
  • 0 comments
  • Fri, 9 Jun
  • play_arrow
  • slideshow

Application Dependency Data Performance Mapping tool - Dynatrace

Chandrish M (@chandrish)

  • Crisp talk for data engineering track
  • Beginner
  • 3 upvotes
  • 0 comments
  • Fri, 9 Jun
  • play_arrow
  • slideshow

Leonardo Machine Learning Foundation - Adding Intelligence to your Enterprise Business

sainath v (@sapcloudengineers)

  • Crisp talk for data engineering track
  • Beginner
  • 2 upvotes
  • 0 comments
  • Fri, 9 Jun
  • play_arrow
  • slideshow

Data in drug discovery

Shefali Lathwal (@shefalilathwal)

  • Full talk for data engineering track
  • Beginner
  • 2 upvotes
  • 1 comments
  • Fri, 9 Jun
  • play_arrow
  • slideshow

Using Probabilistic Data Structures to Build Real-Time Monitoring Dashboards

Rahul Ramesh (@rahul-ramesh-17)

  • Crisp talk for data engineering track
  • Beginner
  • 10 upvotes
  • 0 comments
  • Fri, 9 Jun
  • play_arrow
  • slideshow

Recommendation Engine for Wide Transactions

Harjindersingh Mistry (@harjinder-hari)

  • Full talk for data engineering track
  • Beginner
  • 2 upvotes
  • 0 comments
  • Fri, 9 Jun
  • play_arrow
  • slideshow

Lessons Learnt building and optimizing a self service Data Platform on Apache Spark at Indix

Matild Reema (@matild-reema)

  • Full talk for data engineering track
  • Intermediate
  • 22 upvotes
  • 0 comments
  • Fri, 9 Jun
  • play_arrow
  • slideshow

Unless you measure it; you can’t improve it - Data pipelines for your business KPIs and KRAs

Ketan Khairnar (@ketankhairnar)

  • Workshops
  • Intermediate
  • 10 upvotes
  • 0 comments
  • Thu, 8 Jun
  • play_arrow
  • slideshow

Modeling intent of the user using Probabilistic Machine Learning

Sarah Masud (@sara-02)

  • Full talk for data engineering track
  • Intermediate
  • 4 upvotes
  • 2 comments
  • Wed, 7 Jun
  • play_arrow
  • slideshow

Unlock sub-second SQL analytics over terrabytes of data with Hive and Druid

Nishant Bangarwa (@nishantbangarwa)

  • Full talk for data engineering track
  • Beginner
  • 1 upvotes
  • 1 comments
  • Tue, 6 Jun

How to build scalable and robust data pipeline iteratively.

Danish M (@pixelgenie)

  • Full talk for data engineering track
  • Intermediate
  • 3 upvotes
  • 1 comments
  • Sun, 4 Jun

Talk Less, Chat More

Ashutosh (@ashutrv)

  • Full talk for data engineering track
  • Beginner
  • 2 upvotes
  • 2 comments
  • Fri, 2 Jun
  • play_arrow
  • slideshow

Saving taxes without breaking laws using Machine Learning

GS Jayendran (@vyakyajay)

  • Full talk in Payment Analytics track
  • Beginner
  • 9 upvotes
  • 0 comments
  • Thu, 1 Jun
  • play_arrow
  • slideshow

Reality of Data Modelling: Many analysts, one dataset: Multiple Results

Lakshman Prasad (@becomingguru)

  • Full talk for data engineering track
  • Intermediate
  • 3 upvotes
  • 0 comments
  • Wed, 31 May

Building a Generic but highly customizable and scalable Anomaly Detection System @ Badoo

Akash Mishra (@sleepythread)

  • Full talk for data engineering track
  • Intermediate
  • 6 upvotes
  • 1 comments
  • Tue, 30 May

How to prepare your language for Machine Learning and NLP with an open audio documentation toolkit

Subhashish Panigrahi (@psubhashish)

  • Full talk for Data in Government track
  • Intermediate
  • 4 upvotes
  • 0 comments
  • Sun, 28 May
  • play_arrow
  • slideshow

How to read a user's mind? Designing algorithms for contextual recommendations

Bharath Mohan (@bharathmohan)

  • Crisp talk for data engineering track
  • Beginner
  • 5 upvotes
  • 1 comments
  • Mon, 22 May
  • slideshow

Scalability truths and serverless architectures - why it is harder with stateful, data-driven systems

Regunath Balasubramanian (@regunathb)

  • Full talk for data engineering track
  • Intermediate
  • 3 upvotes
  • 5 comments
  • Mon, 22 May
  • play_arrow

Interestingness of interestingness measures

Simrat Hanspal (@simrathanspal)

  • Full talk for data engineering track
  • Advanced
  • 17 upvotes
  • 7 comments
  • Sun, 30 Apr
  • slideshow

Learnings from building TV viewership platform for 100 Million users at zapr

Agam Jain (@agamj20)

  • Full talk for data engineering track
  • Intermediate
  • 6 upvotes
  • 3 comments
  • Sun, 30 Apr
  • play_arrow
  • slideshow

Seamless Hadoop Deployments - Myth or Reality?

Ragesh Rajagopalan (@rajagopr)

  • Crisp talk for data engineering track
  • Beginner
  • 10 upvotes
  • 3 comments
  • Sun, 30 Apr
  • slideshow

Designing Cost Effective Cloud Native Applications

Tarun Gupta (@tarung)

  • Crisp talk for data engineering track
  • Intermediate
  • 5 upvotes
  • 1 comments
  • Sun, 30 Apr
  • slideshow

Processing mission critical events in real time

Tarun Gupta (@tarung)

  • Crisp talk for data engineering track
  • Intermediate
  • 5 upvotes
  • 3 comments
  • Sun, 30 Apr
  • play_arrow
  • slideshow

Human Centric API Design

Gagan Gupta (@gagangupt16)

  • Crisp talk for data engineering track
  • Beginner
  • 8 upvotes
  • 1 comments
  • Sun, 30 Apr
  • slideshow

Out of Stone age : Why investing in developer tools is necessary for big data development to scale.

Shankar Manian (@shanm)

  • Full talk for data engineering track
  • Intermediate
  • 11 upvotes
  • 1 comments
  • Sat, 29 Apr
  • play_arrow
  • slideshow

Beyond unit tests: Deployment and testing for Hadoop/Spark workflows

Anant Nag (@nntnag17)

  • Full talk for data engineering track
  • Intermediate
  • 11 upvotes
  • 1 comments
  • Fri, 28 Apr
  • play_arrow
  • slideshow

Making data scientists life easy with Docker

Abhishek Kumar (@meabhishekkumar)

  • Full talk for data engineering track
  • Intermediate
  • 14 upvotes
  • 3 comments
  • Fri, 28 Apr
  • play_arrow
  • slideshow

Real-time Monitoring of Big Data Workflows

Akshay Rai (@akshayrai)

  • Full talk for data engineering track
  • Intermediate
  • 12 upvotes
  • 4 comments
  • Fri, 28 Apr
  • play_arrow
  • slideshow

Causal Analytics in Retail and Telco

Gaurav Goswami (@gauravgoswami)

  • Crisp talk for data engineering track
  • Intermediate
  • 2 upvotes
  • 10 comments
  • Fri, 28 Apr
  • slideshow

Dr. Elephant: Achieving Quicker, Easier, and Cost-effective Big Data Analytics

Akshay Rai (@akshayrai)

  • Crisp talk for Data in Government track
  • Intermediate
  • 11 upvotes
  • 2 comments
  • Thu, 27 Apr
  • play_arrow
  • slideshow

Using data pipelines to navigate your data ocean

Vipul Mathur (@vipulmathur)

  • Full talk for data engineering track
  • Beginner
  • 15 upvotes
  • 2 comments
  • Thu, 27 Apr
  • play_arrow

The Python ecosystem for data science - Landscape Overview

Ananth Krishnamoorthy (@akrishnamoorthy)

  • Full talk for data engineering track
  • Beginner
  • 8 upvotes
  • 3 comments
  • Thu, 27 Apr
  • play_arrow
  • slideshow

A Recommender for Match-making: Item-based CF, PageRank, Evaluation techniques & Deep-Learning

prabhakar srinivasan (@prabhacar7)

  • Full talk for data engineering track
  • Advanced
  • 8 upvotes
  • 3 comments
  • Thu, 27 Apr

Search Infrastructure @ Slack using Lambda Architecture

Ananth Durai (@vananth22)

  • Full talk for data engineering track
  • Intermediate
  • 9 upvotes
  • 5 comments
  • Thu, 27 Apr
  • play_arrow
  • slideshow

Discovery tools for Government data analytics

Venkateswaran M (@venkateswaranm)

  • Crisp talk for Data in Government track
  • Intermediate
  • 8 upvotes
  • 4 comments
  • Tue, 25 Apr
  • play_arrow
  • slideshow

Autonomous Grid using Machine Learning

Charan Puvvala (@charanpuvvala)

  • Full talk for data engineering track
  • Intermediate
  • 6 upvotes
  • 2 comments
  • Tue, 25 Apr

Optimising Model performance using automated ML pipeline for predicting purchase propensity @ Fractal Analytics

PadmaCh (@padmach)

  • Full talk for data engineering track
  • Advanced
  • 34 upvotes
  • 6 comments
  • Tue, 25 Apr
  • play_arrow
  • slideshow

Application of machine learning in oil and gas industry

Priyanka Raghavan (@priyankaraghavan)

  • Crisp talk for data engineering track
  • Beginner
  • 14 upvotes
  • 3 comments
  • Tue, 25 Apr
  • play_arrow
  • slideshow

Application of AI in e-commerce industry from product search to customer satisfaction

Dr Amit Garg (@garg78)

  • Crisp talk for data engineering track
  • Intermediate
  • 26 upvotes
  • 4 comments
  • Sat, 22 Apr

Learning representations of text for NLP

Anuj Gupta (@anujgupta82)

  • Workshops
  • Intermediate
  • 37 upvotes
  • 3 comments
  • Wed, 19 Apr
  • slideshow

Big Data Computations: Comparing Apache HAWQ, Druid, Google Spanner and GPU Databases

Vijay Srinivas Agneeswaran, Ph.D (@vijayagneeswaran)

  • Full talk for data engineering track
  • Intermediate
  • 6 upvotes
  • 3 comments
  • Tue, 18 Apr
  • play_arrow
  • slideshow

Working with Apache Spark in Eta

Jyothsna Srinivas (@jyothsnasrinivas)

  • Full talk for data engineering track
  • Intermediate
  • 18 upvotes
  • 1 comments
  • Sun, 16 Apr

ML For Personalization At Scale @ Nearbuy

ankit kohli (@ankitko)

  • Full talk for data engineering track
  • Advanced
  • 23 upvotes
  • 2 comments
  • Wed, 12 Apr
  • play_arrow
  • slideshow

Credit where Credit is due: Using data science to lend to customers without a credit history

Vanitha DSilva (@vanithadsilva)

  • Crisp talk for data engineering track
  • Intermediate
  • 16 upvotes
  • 4 comments
  • Tue, 11 Apr
  • play_arrow
  • slideshow

micro-ATMs: The what, the why and the how

Vanitha DSilva (@vanithadsilva)

  • Full talk in Payment Analytics track
  • Intermediate
  • 15 upvotes
  • 4 comments
  • Tue, 11 Apr
  • play_arrow
  • slideshow

Machine Learning Applications in Cisco Spark Collaboration SaaS

Narayanan Subramaniam (@narayanan-subramaniam)

  • Crisp talk for data engineering track
  • Intermediate
  • 1 upvotes
  • 2 comments
  • Sat, 8 Apr

How to engineer a personalization system that can handle Paytm scale

Harinder Takhar (@harindertakhar) (proposing)

  • Full talk for data engineering track
  • Advanced
  • 11 upvotes
  • 2 comments
  • Tue, 4 Apr

How Paytm uses k8s for global expansion

Pranshu Saxena (@pranshus)

  • Full talk for data engineering track
  • Intermediate
  • 3 upvotes
  • 6 comments
  • Tue, 4 Apr

Large scale business stats aggregation using Kafka

Vinothkumar Raman (@vinothkumarraman)

  • Full talk of 40 mins duration
  • Intermediate
  • 22 upvotes
  • 2 comments
  • Thu, 30 Mar
  • play_arrow
  • slideshow

Blockchain for business and government

Mani Madhukar (@manimadhukar)

  • Crisp talk for Data in Government track
  • Beginner
  • 4 upvotes
  • 5 comments
  • Mon, 20 Mar
  • play_arrow
  • slideshow

Streaming for life, universe and everything using Confluent Platform

Aastha Rai (@aastha0304)

  • Crisp talk for data engineering track
  • Intermediate
  • 6 upvotes
  • 4 comments
  • Tue, 14 Mar