Submissions

The Fifth Elephant 2017

On data engineering and application of ML in diverse domains

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Submissions

PS

Pranshu Saxena

How Paytm uses k8s for global expansion

At Paytm, we are constantly engaged in creating new environments and aligning infrastructure for standard services such as Authentication, Access, Logging/Monitoring etc. There is also the case of dy…more
  • 7 comments
  • Rejected
  • Tue, 04 Apr
Section: Full talk for data engineering track Technical level: Intermediate
VR

Vinothkumar Raman

Large scale business stats aggregation using Kafka

At Indix we collect and process lot of data. We monitor the correct behaviour of our system through collection of business metrics. Over the time, we moved most of our system from batch map-reduce jo…more
  • 2 comments
  • Rejected
  • Thu, 30 Mar
Technical level: Intermediate
ak

ankit kohli

ML For Personalization At Scale @ Nearbuy

Here I will try to explain how we use ML to give personalized recommendations to the customers. Also I will explain how have we setup our Big Data Pipeline using KAFKA , SPARK and HBASE . The amount …more
  • 4 comments
  • Rejected
  • Wed, 12 Apr
Section: Full talk for data engineering track Technical level: Advanced
NS

Narayanan Subramaniam

Machine Learning Applications in Cisco Spark Collaboration SaaS

A use case driven technical overview of the applications of machine learning in the Cisco Spark Collaboration SaaS offer, including Webex (refer: http://www.ciscospark.com)more
  • 2 comments
  • Rejected
  • Sat, 08 Apr
Section: Crisp talk for data engineering track Technical level: Intermediate
VD

Vanitha DSilva

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

The aftermath of demonetization has led to a scramble for digital or cashless payments. Enter the era of the micro-ATM, the superhero of payment devices and a solution to the dearth of ATM networks i…more
  • 5 comments
  • Rejected
  • Tue, 11 Apr
Section: Full talk in Payment Analytics track Technical level: Intermediate
CP

Charumitra Pujari

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

At paytm we value user experience and we want to pre-emptively show a user the types of products they would want to buy. In this talk, we will walk our audience through how we personalize every pixel…more
  • 2 comments
  • Confirmed & scheduled
  • Tue, 04 Apr
Section: Full talk in Payment Analytics track Technical level: Advanced
MM

Mani Madhukar

Blockchain for business and government

The talk will focus on how Blockchain technology has matured to infuse trust based systems and hence apt for implementation in Businesses and government programs. We will also focus on the early adop…more
  • 5 comments
  • Rejected
  • Mon, 20 Mar
Section: Crisp talk for Data in Government track Technical level: Beginner
Harinder Takhar (@harindertakhar) (proposing)

How to engineer a personalization system that can handle Paytm scale

When we say we value customer experience we meant it! When you have to personalize every pixel on the app, your standard caching techniques go out of window and you need very fast and scalable system…more
  • 2 comments
  • Rejected
  • Tue, 04 Apr
Section: Full talk for data engineering track Technical level: Advanced
VP

Vijay Srinivas Agneeswaran, Ph.D

Distributed Consensus and Data Safety: NewSQL Perspective

We explore data safety issues in designing large distributed systems. Though data safety issues have been addressed in traditional complex software systems such as aircraft engineering systems, ensur…more
  • 5 comments
  • Confirmed & scheduled
  • Tue, 18 Apr
Section: Full talk for data engineering track Technical level: Intermediate
VD

Vanitha DSilva

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

Traditional loans are based on banking history leaving a large segment of people ineligible. These however, represent a highly untapped segment representing large purchasing potential. How do you dee…more
  • 4 comments
  • Waitlisted
  • Tue, 11 Apr
Section: Crisp talk for data engineering track Technical level: Intermediate
VP

Vijay Srinivas Agneeswaran, Ph.D

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

A class of big data computations known as the distributed merge tree was required to be built to aggregate user information across multiple data sources in media domain. This class is characterized b…more
  • 9 comments
  • Rejected
  • Tue, 18 Apr
Section: Full talk for data engineering track Technical level: Intermediate
AG

Anuj Gupta

Learning representations of text for NLP

Think of your favorite NLP application you wish to build - sentiment analysis, named entity recognition, machine translation, information extraction, summarization, recommender system. A key step in …more
  • 5 comments
  • Rejected
  • Wed, 19 Apr
Section: Workshops Technical level: Intermediate
JS

Jyothsna Srinivas

Working with Apache Spark in Eta

Eta is a high-level, purely functional programming language and also the newest member to the JVM world. It has been gaining traction as an alternative to Scala for solving Big Data problems. In this…more
  • 3 comments
  • Cancelled
  • Sun, 16 Apr
Section: Full talk for data engineering track Technical level: Intermediate
GC

Govind Chandrasekhar

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

Product matching is the challenge of examining two different representations of retail products (think items that you see on e-commerce websites) and determining whether they both refer to the same p…more
  • 3 comments
  • Confirmed & scheduled
  • Sat, 29 Apr
Section: Full talk for data engineering track Technical level: Intermediate
AK

Ananth Krishnamoorthy

The Python ecosystem for data science - Landscape Overview

In their day-to-day jobs, data science teams and data scientists face challenges in many overlapping yet distinct areas such as Reporting, Data Processing & Storage, Scientific Computing, ML Modellin…more
  • 4 comments
  • Rejected
  • Thu, 27 Apr
Section: Full talk for data engineering track Technical level: Beginner
GG

Gagan Gupta

Human Centric API Design

In the last decade, with the advent of big data technologies, the amount of data produced and processed is increasing exponentially. This data is meaningless if the insights out of it are not exposed…more
  • 1 comments
  • Rejected
  • Sun, 30 Apr
Section: Crisp talk for data engineering track Technical level: Beginner
PM

Paul Meinshausen

Designing Machine Learning Pipelines for Mining Transactional SMS Messages

Much of data science involves using data for some practical, business purpose. The data usually needs to be cleaned and processed and that might take a while, but it is generally close to where it ne…more
  • 2 comments
  • Confirmed & scheduled
  • Fri, 28 Apr
Section: Full talk for data engineering track Technical level: Intermediate
RB

Ramanan Balakrishnan

Machine Learning from Practice to Production

With AI research and machine learning systems growing at great speed, companies require significant effort to keep up or risk losing their relevance in this brave new world. The new tide also brings …more
  • 2 comments
  • Confirmed & scheduled
  • Tue, 25 Apr
Section: Full talk for data engineering track Technical level: Beginner
TG

Tarun Gupta

Designing Cost Effective Cloud Native Applications

Designing applications for cloud environment requires thinking design in a different paradigm. In this talk, I will be discussing design principles, taking examples of applications that we have devel…more
  • 1 comments
  • Rejected
  • Sun, 30 Apr
Section: Crisp talk for data engineering track Technical level: Intermediate
VM

Vipul Mathur

Using data pipelines to navigate your data ocean

One of the main challenges facing companies adopting data-driven analytics-based approach to their business, is how to scale the development and adoption of data products throughout the company. In o…more
  • 2 comments
  • Rejected
  • Thu, 27 Apr
Section: Full talk for data engineering track Technical level: Beginner
SM

Shankar Manian

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

Do you wish hadoop development was as easy as any other application development ? Do you wish we had comprehensive tools that are well-integrated with each other for hadoop development ? At linkedin,…more
  • 1 comments
  • Rejected
  • Sat, 29 Apr
Section: Full talk for data engineering track Technical level: Intermediate
AR

Akshay Rai

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

Open Source: https://github.com/linkedin/dr-elephantmore
  • 2 comments
  • Rejected
  • Thu, 27 Apr
Section: Crisp talk for Data in Government track Technical level: Intermediate
SG

Santosh GSK

Adapting Bandit Algorithms to optimise user experience at Practo Consult

The art of trading between exploiting the best arm versus exploring for further knowledge of other arms has long been studied as Bandit Algorithms in various fields of clinical trials, designing fina…more
  • 4 comments
  • Confirmed & scheduled
  • Sun, 30 Apr
Section: Crisp talk for data engineering track Technical level: Intermediate
VM

Venkateswaran M

Discovery tools for Government data analytics

This talk will focus on Data discovery tools such as Tableau and Qlikview in the context of Government data. Invariably, the Government data is complex and most of the efforts are focused on getting …more
  • 8 comments
  • Rejected
  • Tue, 25 Apr
Section: Crisp talk for Data in Government track Technical level: Intermediate
BK

Bhargav Kowshik

Gabbar: Machine learning to guard OpenStreetMap

OpenStreetMap is the largest free and open map of the world! An average of 2 million features are touched by volunteers around the world every single day. Amazing isn’t it? The global scale and the l…more
  • 5 comments
  • Confirmed & scheduled
  • Sun, 30 Apr
Section: Full talk for data engineering track Technical level: Intermediate
SR

Sriram R

Suuchi - Toolkit to build distributed systems

At Indix, we have a bunch of services that need to operate on top of large volume of product data. We started out with using open source distributed systems (like Hadoop, HBase, Solr, Spark, etc) to …more
  • 9 comments
  • Confirmed & scheduled
  • Wed, 26 Apr
Section: Full talk for data engineering track Technical level: Intermediate
GG

Gaurav Godhwani

Transforming India's Budgets into Open Linked Data

Indian Budget documents across various tiers of government, consist of detailed information on allocations made and resources raised in a financial year. Unfortunately these documents are published i…more
  • 4 comments
  • Confirmed & scheduled
  • Sun, 30 Apr
Section: Full talk for Data in Government track Technical level: Intermediate
AD

Ananth Durai

Search Infrastructure @ Slack using Lambda Architecture

Slack is a collaboration tool for teams. We’re on a mission to make your working life simpler, more pleasant, and more productive. Search is the core feature of Slack offerings as Slack itself is an …more
  • 5 comments
  • Cancelled
  • Thu, 27 Apr
Section: Full talk for data engineering track Technical level: Intermediate
DG

Dr Amit Garg

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

Artificial Intelligence(AI) was introduced to develop and create “thinking machine” that are capable of mimicking, learning and replacing human intelligence. Since last 20 years, AI has shown great p…more
  • 5 comments
  • Rejected
  • Sat, 22 Apr
Section: Crisp talk for data engineering track Technical level: Intermediate
GG

Gaurav Goswami

Causal Analytics in Retail and Telco

In this talk, I will discuss causal analytics using machine learning in the retail and telco domains. This talk should provide a brief overview of the value machine learning can provide in these doma…more
  • 10 comments
  • Rejected
  • Fri, 28 Apr
Section: Crisp talk for data engineering track Technical level: Intermediate
AK

Abhishek Kumar

Making data scientists life easy with Docker

Life of data scientists is hard as they have to bother not only about the algorithms & analysis but also about the environment & dependencies they have to build in order to get there at the first pla…more
  • 5 comments
  • Rejected
  • Fri, 28 Apr
Section: Full talk for data engineering track Technical level: Intermediate
CP

Charan Puvvala

Autonomous Grid using Machine Learning

In this talk we deep dive into how we are assisting Energy Utilities using IOT and Machine Learning to build the next generation of Autonomous grid. The potential impact of applying Machine Learning,…more
  • 2 comments
  • Rejected
  • Tue, 25 Apr
Section: Full talk for data engineering track Technical level: Intermediate
AN

Anant Nag

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

As a Hadoop developer, do you want to quickly develop your Hadoop/Spark workflows? Do you want to test your workflows in a sandboxed environment similar to production? Do you want to write unit tests…more
  • 5 comments
  • Rejected
  • Fri, 28 Apr
Section: Full talk for data engineering track Technical level: Intermediate
TG

Tarun Gupta

Processing mission critical events in real time

If you have an event driven mission-critical application, you are always worried about such application failing and leading to opportunity or revenue loss. For a data based adtech company like Zapr M…more
  • 3 comments
  • Rejected
  • Sun, 30 Apr
Section: Crisp talk for data engineering track Technical level: Intermediate
ps

prabhakar srinivasan

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

Online match-making has a lot of challenges where Machine-Learning can help. When we look at a profile what is it that makes us swipe right or left? Is there something about a profile that attracts u…more
  • 9 comments
  • Rejected
  • Thu, 27 Apr
Section: Full talk for data engineering track Technical level: Advanced
AR

Akshay Rai

Real-time Monitoring of Big Data Workflows

Do you want to know the real-time status of your big data job? Not sure of how to collect all the metrics from these jobs and make sense out of them? Want to track and monitor the metrics in real tim…more
  • 4 comments
  • Rejected
  • Fri, 28 Apr
Section: Full talk for data engineering track Technical level: Intermediate
PR

Priyanka Raghavan

Application of machine learning in oil and gas industry

This talk describes the various machine learning algorithms used in the public SEG (Society of Exploration Geophysicist) challenge held in December 2016 to identify lithofacies based on well log meas…more
  • 3 comments
  • Rejected
  • Tue, 25 Apr
Section: Crisp talk for data engineering track Technical level: Beginner
RR

Ragesh Rajagopalan

Seamless Hadoop Deployments - Myth or Reality?

Continuous deployment of hadoop workflows is by and large a distant dream for every hadoop engineer. Reducing wastage of compute resources, improving developer productivity, eliminating costly bugs a…more
  • 3 comments
  • Rejected
  • Sun, 30 Apr
Section: Crisp talk for data engineering track Technical level: Beginner
MK

Manas Ranjan Kar

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

Serverless is the new kid on the block, and an exciting one at that ! As Anand Chitipothu puts it, it’s rapidly becoming the Uber of cloud computing resources.more
  • 4 comments
  • Confirmed & scheduled
  • Sun, 30 Apr
Section: Crisp talk for data engineering track Technical level: Intermediate
SS

Srini V. Srinivasan

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

In this talk, we will describe key real-time use cases in the areas of fraud detection, risk management and revenue assurance for payment systems and other such related systems. We will then present …more
  • 0 comments
  • Confirmed & scheduled
  • Thu, 27 Apr
Section: Full talk in Payment Analytics track Technical level: Intermediate
P

PadmaCh

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

Ensemble learning is the process by which multiple machine-learning models are evaluated and combined to help build a combined model that provides better results. Building these models require experi…more
  • 6 comments
  • Rejected
  • Tue, 25 Apr
Section: Full talk for data engineering track Technical level: Advanced
RB

Regunath Balasubramanian

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

In system building, data store choices affect system scalability more often than language platforms. Frequently it is also the single most constrained resource in the application stack. While most da…more
  • 2 comments
  • Confirmed & scheduled
  • Mon, 22 May
Section: Full talk for data engineering track Technical level: Intermediate
RR

Ramprakash R

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

Today ML/AI is being used in mission critical applications. However, it is still difficult for a human being to trust a black-boxy ML algorithm. Wouldn’t it be cool if an algorithm could also explain…more
  • 4 comments
  • Confirmed & scheduled
  • Mon, 22 May
Section: Crisp talk for data engineering track Technical level: Intermediate
KS

Krishnapriya Satagopan

Plumbing data science pipelines

Data - There is a lot of it . But organizing it can be challenging, and analysis/consumption cannot begin until data is aggregated and massaged into compatible formats. These challenges grow more dif…more
  • 3 comments
  • Confirmed & scheduled
  • Mon, 22 May
Section: Crisp talk for data engineering track Technical level: Intermediate
SH

Simrat Hanspal

Interestingness of interestingness measures

Analysis of relationship between entities is at the heart of data mining problems. There are many metrics used for association mining like support, confidence, lift, mutual information etc. However m…more
  • 7 comments
  • Under evaluation
  • Sun, 30 Apr
Section: Full talk for data engineering track Technical level: Advanced
RB

Regunath Balasubramanian

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

Building scalable systems is not easy. It is not as simple as deploying on a cloud and expecting it to scale alongwith the cloud’s elasticity. Many systems and solutions that claim elasticity of scal…more
  • 5 comments
  • Rejected
  • Mon, 22 May
Section: Full talk for data engineering track Technical level: Intermediate
AJ

Agam Jain

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

Zapr Media Labs has come a long way from tracking TV viewership of around 5 Million users two years back to around 100 Million users currently. We want to share learnings while building a complex aud…more
  • 3 comments
  • Rejected
  • Sun, 30 Apr
Section: Full talk for data engineering track Technical level: Intermediate
AD

Amit Doshi

Developing and Deploying Analytics for Internet of Things (IoT)

The combination of smart connected devices with data analytics and machine learning is enabling a wide range of applications, from home-grown traffic monitors to sophisticated predictive maintenance …more
  • 7 comments
  • Confirmed & scheduled
  • Mon, 22 May
Section: Sponsored session Technical level: Intermediate
BM

Bharath Mohan

Do you know what's on TV?

The mobile has made tremendous progress - but it is still referred as “second screen” to the Television. Television (specifically Linear TV) will continue to be the most efficient way to get high qua…more
  • 10 comments
  • Confirmed & scheduled
  • Mon, 22 May
Section: Full talk for data engineering track Technical level: Intermediate
SP

Subhashish Panigrahi

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

Pronunciation libraries are a key to building machine learning tools and many Natural Language Processing research and product development. In the age of personal assistant apps, human voice-based ap…more
  • 0 comments
  • Rejected
  • Sun, 28 May
Section: Full talk for Data in Government track Technical level: Intermediate
AS

Anand S

What explains our marks?

The NCERT put together a large-scale survey called the National Achievement Survey. This captured student performance across 4 subjects via 100 questions each, the demographics and behaviour of stude…more
  • 3 comments
  • Confirmed & scheduled
  • Wed, 24 May
Section: Crisp talk for Data in Government track Technical level: Beginner
BM

Bharath Mohan

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

The human mind is going through thousands of thoughts everyday. A perfect recommender system needs to know what is going on and suggest something useful - at all times, without being perceived as int…more
  • 1 comments
  • Rejected
  • Mon, 22 May
Section: Crisp talk for data engineering track Technical level: Beginner
GJ

GS Jayendran

Saving taxes without breaking laws using Machine Learning

Novel use cases for machine learning in the taxation and accounting areas. These are particularly important given the push towards GST and digitization of taxes in India.more
  • 1 comments
  • Rejected
  • Thu, 01 Jun
Section: Full talk in Payment Analytics track Technical level: Beginner
VS

Vimal Sharma

Apache Atlas Introduction: Need for Governance and Metadata management

Apache Atlas is the one stop solution for data governance and metadata management on enterprise Hadoop clusters. Atlas has a scalable and extensible architecture which can plug into many Hadoop compo…more
  • 4 comments
  • Confirmed & scheduled
  • Fri, 26 May
Section: Full talk for data engineering track Technical level: Intermediate
LP

Lakshman Prasad

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

There is a study that gave the same data set to many teams competent to analyse it and asked them all the same question: “whether soccer referees are more likely to give red cards to dark skin toned …more
  • 0 comments
  • Rejected
  • Wed, 31 May
Section: Full talk for data engineering track Technical level: Intermediate
DS

Dharma Shukla

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

Description: Dharma and his team has spent past 7 years to build Azure Cosmos DB (http://cosmosdb.com) - a massively scalable, multi-tenant, globally distributed database service from the ground up. …more
  • 9 comments
  • Confirmed & scheduled
  • Fri, 26 May
Section: Full talk for data engineering track Technical level: Intermediate
RD

Rakesh Dubbudu

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

This talk will cover: The challenges involved in opening up government data.more
  • 0 comments
  • Confirmed & scheduled
  • Wed, 12 Jul
Section: Full talk for Data in Government track Technical level: Beginner
KS

Kumar Shubham

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

Matching images with human-like accuracy is typically extremely expensive. A lot of GPU resources and training data are required for the deep-learning model to perform image-matching. While GPU is so…more
  • 2 comments
  • Confirmed & scheduled
  • Fri, 09 Jun
Section: Crisp talk for data engineering track Technical level: Intermediate
A

Ashutosh

Talk Less, Chat More

Conversational interfaces are the new channels coming up for business. These channels are new for both users and businesses. For a business it’s a new kind of user behaviour they have to understand! …more
  • 4 comments
  • Rejected
  • Fri, 02 Jun
Section: Full talk for data engineering track Technical level: Beginner
NB

Nishant Bangarwa

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

Druid is an open-source analytics data store designed for business inteligence OLAP queries on timeseries data. Druid provides low latency real-time data ingestion, flexible data exploration and fast…more
  • 2 comments
  • Rejected
  • Wed, 07 Jun
Section: Full talk for data engineering track Technical level: Beginner
KK

Ketan Khairnar

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

Abstract Any business can gain unfair advantage through actionable insights using data pipelines and some common sense. We’re already experiencing this through our interactions online (amazon , mediu…more
  • 6 comments
  • Rejected
  • Thu, 08 Jun
Section: Workshops Technical level: Intermediate
MR

Matild Reema

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

In this talk I will talk about how we used Apache Spark to build a self service data platform at Indix that helped democratise access to several datasets at Indix to our customers and the internal en…more
  • 3 comments
  • Rejected
  • Fri, 09 Jun
Section: Full talk for data engineering track Technical level: Intermediate
RR

Rahul Ramesh

Using Probabilistic Data Structures to Build Real-Time Monitoring Dashboards

Performing basic operations like finding an element in a set or calculating its cardinality for a few thousands of data points is child’s play. However, it becomes complex and prohibitively expensive…more
  • 0 comments
  • Rejected
  • Fri, 09 Jun
Section: Crisp talk for data engineering track Technical level: Beginner
AC

Anand Chitipothu

Distributed Machine Learning - Challenges and Oppurtunities

The traditional machine learning libraries like scikit-learn in Python are written to work on a single computer. While that is good enough for small datasets, traning ML models on large datasets ofte…more
  • 0 comments
  • Confirmed & scheduled
  • Sat, 10 Jun
Section: Crisp talk for data engineering track Technical level: Intermediate
NB

Nishant Bangarwa

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

When interacting with analytics dashboards in order to achieve a smooth user experience, two major key requirements are quick response time and data freshness. To meet the requirements of creating fa…more
  • 1 comments
  • Confirmed & scheduled
  • Wed, 07 Jun
Section: Full talk for data engineering track Technical level: Intermediate
sv

sainath v

Leonardo Machine Learning Foundation - Adding Intelligence to your Enterprise Business

Machine learning and the larger world of artificial intelligence (AI) are no longer martial arts. As a new breed of software that is able to learn without being explicitly programmed, machine learnin…more
  • 0 comments
  • Rejected
  • Fri, 09 Jun
Section: Crisp talk for data engineering track Technical level: Beginner
PN

Preeti Negi

ML Goes Fruitful

Industry is demanding for the real-time interactions, automation[2] and decision making. The latest trends like machine learning, Internet of Things, Artificial Intelligence, Virtual Reality, Digitiz…more
  • 2 comments
  • Rejected
  • Sat, 10 Jun
Section: Workshops Technical level: Beginner
RM

Rajaram Mallya

Democratising Data in the Microservices World

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 runn…more
  • 1 comments
  • Rejected
  • Sat, 10 Jun
Section: Full talk for data engineering track Technical level: Intermediate
BS

Bargava Subramanian

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 sc…more
  • 1 comments
  • Confirmed
  • Tue, 30 May
Section: Workshops Technical level: Beginner
kM

krupal Modi

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

Evolution of automated messaging, which started in 1966 with first Chatbot, ELIZA, has now reached a stage where Chatbots have found there application in several industry domains like personal assist…more
  • 2 comments
  • Rejected
  • Fri, 09 Jun
Section: Full talk for data engineering track Technical level: Intermediate
SL

Shefali Lathwal

Data in drug discovery

Data is being used to solve some of the greatest challenges in medicine today. Advances in technology mean that scientists have access to data that was impossible to acquire just 5 years ago. Modelin…more
  • 2 comments
  • Rejected
  • Fri, 09 Jun
Section: Full talk for data engineering track Technical level: Beginner
DA

Deepikavalli A

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

The Business Intelligence (BI) landscape is constantly in a state of flux – there is a need for constant growth in order to cope with the exponential changes in the data and analytics space. In today…more
  • 2 comments
  • Rejected
  • Sat, 10 Jun
Section: Crisp talk for data engineering track Technical level: Intermediate
PM

Prakash Mall

Multi-channel conversational chatbot platform powered by NLP engine

In this talk, the speaker would talk about a chat engine/ platform to enable human to machine interaction on multiple channels (web, slack, hipchat, etc) including social like facebook across text an…more
  • 1 comments
  • Rejected
  • Sat, 10 Jun
Section: Crisp talk for data engineering track Technical level: Beginner
NS

Nitin Saraswat

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

Have you ever wondered what could you do with the piece of paper that you have at hand when you make a purchase at your local grocery store, get your car’s tank full, see a doctor when you are ill, g…more
  • 0 comments
  • Rejected
  • Sat, 10 Jun
Section: Full talk for data engineering track Technical level: Intermediate
DM

Danish M

How to build scalable and robust data pipeline iteratively.

I will drill down to understand how startups can build scalable data pipeline using open source tools. What do all these tools do and how do they fit into the ecosystem? And how to iteratively build …more
  • 2 comments
  • Rejected
  • Sun, 04 Jun
Section: Full talk for data engineering track Technical level: Intermediate
CM

Chandrish M

Application Dependency Data Performance Mapping tool - Dynatrace

More companies today are adopting cloud services and related technologies like microservices architecture and containerization to build and deliver digital services faster and achieve greater IT agil…more
  • 0 comments
  • Rejected
  • Fri, 09 Jun
Section: Crisp talk for data engineering track Technical level: Beginner
UP

Umesh Prasad

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

Key Take aways of the talk 0. Demystifying Lucene & showing inside view of it and how to extend core components of it. 1. Deployed in Production @ Flipkart. 2. Served 10K reqs/sec search (no cache) &…more
  • 1 comments
  • Confirmed & scheduled
  • Fri, 09 Jun
Section: Full talk for data engineering track Technical level: Intermediate
NP

Nabarun Pal

Building camera based intelligent applications

Camera based intelligent applications are lot of fun! There are many practical applications of it like Industrial Counters, Real Time Object Tracking, Object Classification, Road Traffic Estimation e…more
  • 0 comments
  • Rejected
  • Sat, 10 Jun
Section: Crisp talk for data engineering track Technical level: Intermediate
HM

Harjindersingh Mistry

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 a…more
  • 0 comments
  • Rejected
  • Fri, 09 Jun
Section: Full talk for data engineering track Technical level: Beginner
an

anugrah nayar

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 driver…more
  • 0 comments
  • Awaiting details
  • Sat, 10 Jun
Section: Full talk for data engineering track Technical level: Beginner
RS

Rasagy Sharma

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

A talk on visualizing data with maps, with an aim to answer the following questions:more
  • 0 comments
  • Confirmed & scheduled
  • Sat, 10 Jun
Section: Full talk for data engineering track Technical level: Intermediate
DS

Deva P. Seetharam

Bits and joules: data-driven energy systems

The electricity industry is going through a paradigm shift by moving from centralised generation to distributed energy resources. This talk will give an overview of this shift, discuss how data-drive…more
  • 0 comments
  • Confirmed & scheduled
  • Tue, 25 Jul
Section: Full talk for Data in Government track Technical level: Beginner
David Sangma (@davidsangma) (proposing)

Building a converged platform for data analytics

This talk will explain the approaches one must take to build a converged platform for data analytics. We at IQLECT have built a real-time analytics platform and will like to share the experience. Als…more
  • 2 comments
  • Rejected
  • Mon, 12 Jun
Section: Crisp talk for data engineering track Technical level: Advanced
ZC

Zainul Charbiwala

How We Built Our Machine Intelligence To Help Humans Save Lives

7.2 million people die of heart disease every year. 50% of these lives can be saved if heart attacks can be diagnosed quickly and treatment coordinated within the golden hour. Diagnosing heart diseas…more
  • 0 comments
  • Confirmed & scheduled
  • Sat, 22 Jul
Section: Full talk for Data in Government track Technical level: Beginner
AK

Amit Kapoor

Interactive Data Visualisation using Markdown

“A picture is worth a thousand words. An interface is worth a thousand pictures.” — Ben Shneidermanmore
  • 0 comments
  • Confirmed & scheduled
  • Mon, 12 Jun
Section: Full talk for data engineering track Technical level: Beginner
AR

Aastha Rai

Streaming for life, universe and everything using Confluent Platform

When Kafka came it made streaming and our lives a lot easier. But there were still some gaps to fill, how to validate the schema of events coming in, how to stream data from languages other than java…more
  • 4 comments
  • Rejected
  • Tue, 14 Mar
Section: Crisp talk for data engineering track Technical level: Intermediate
AM

Akash Mishra

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

Badoo is a data driven company with 340 million users across 190 countries it provides a number of apps and white label services across multiple platforms. Badoo crunches through around 23 billion ev…more
  • 3 comments
  • Rejected
  • Wed, 31 May
Section: Full talk for data engineering track Technical level: Intermediate
SM

Sarah Masud

Modeling intent of the user using Probabilistic Machine Learning

Understanding the user’s intent can help the product team dramatically improve the user’s experience. Be it adding the right products to a shopping cart, stocks to the portfolio or packages to a soft…more
  • 3 comments
  • Cancelled
  • Wed, 07 Jun
Section: Full talk for data engineering track Technical level: Intermediate

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The Fifth Elephant - known as one the best #datascience and #machinelearning conference in Asia - is transitioning into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices.more