Jul 2016
25 Mon
26 Tue
27 Wed
28 Thu 08:30 AM – 06:25 PM IST
29 Fri 08:30 AM – 06:15 PM IST
30 Sat 08:45 AM – 05:00 PM IST
31 Sun 08:15 AM – 06:00 PM IST
Jul 2016
25 Mon
26 Tue
27 Wed
28 Thu 08:30 AM – 06:25 PM IST
29 Fri 08:30 AM – 06:15 PM IST
30 Sat 08:45 AM – 05:00 PM IST
31 Sun 08:15 AM – 06:00 PM IST
Auditorium 1
Auditorium 2
Auditorium 3
Room 1
Hall
C5 (Seminar Hall)
Training Room
08:30–09:20
Check-in and breakfast
09:20–09:30
Introduction to The Fifth Elephant 2016
09:30–10:30
Using Data to Identify the Genomic Cause of Disease
Ramesh Hariharan, Strand Life Sciences
09:35–10:20
Allocation and Forecasting in Guaranteed Delivery of Advertisements
Aditya Ramana Rachakonda, Flipkart
10:30–11:30
Keynote: The Alternative Data revolution on Wall St
Gene Ekster, Analyst and Investment Consultant
11:30–12:00
Morning tea break
12:00–12:45
ML in fin-tech - Transforming 60 crore Indian lives
Riddhi Mittal, Finomena
12:00–12:45
Purpose, Speed & Visibility : Facilitating product discovery & engagement on a e-commerce website
Ekta Grover, BloomReach Inc
12:45–13:30
Deciphering Driving Behaviour using Geospatial Temporal Data Collected from Smartphone Sensors
Aditya Karnik, Zendrive
12:45–13:30
Taking Fashion and Lifestyle Commerce Towards SKUs Using Deep Image and Text Parsing
Vijay Gabale, Infilect
13:30–14:40
Lunch
14:40–15:00
Data-Driven Decision Making in Indian Agriculture: the Present and the Future
Udit Poddar, SocialCops
14:40–15:00
RightFit- A Data Science Approach to Reduce Product Returns in Fashion e-Commerce
Ashish Kulkarni, JabongLabs
15:00–16:00
What do machine learning and high performance computing have to do with big cats in the wild?
Dr. Arjun Gopalaswamy, Indian Statistical Institute, Bangalore
15:00–15:45
Scaling the Largest Functional DataSet @Flipkart aka Catalog
Anuj Mittal, Flipkart
15:00–16:00
Birds of Feather (BoF): How much math and statistics programmers need to know to hack their way into Machine Learning?
Facilitators: Amit Kapoor, Bargava S., Ashwin Kumar
15:45–16:05
Increasing Trust and Efficiency of Data Science using dataset versioning
Venkata Pingali, Scribble Data
16:00–16:05
Birds of Feather (BoF): Alternative Data
Facilitators: Gene Ekster, Udit Poddar
16:05–16:40
Evening tea break
16:40–17:25
Hierarchical Structure, Hierarchical Bayes approach and implementation of MCMC
Soumen Dey, Indian Statistical Institute, Bangalore
16:40–17:25
Taking Analytics Applications from Labs to the Real World: Transfer Learning in Practice
Shourya Roy, Xerox Research
17:25–18:25
Djembe Jam
Auditorium 1
Auditorium 2
Auditorium 3
Room 1
Hall
C5 (Seminar Hall)
Training Room
08:30–09:20
Check-in and breakfast
09:20–09:30
Summary of day 1
10:15–11:00
Meet the needs of content marketing with the power of NLP
Balaji Vasan, Adobe Research Big Data Experience Labs
10:15–10:45
Let your Big Data Processing take flight with Apache Falcon
Pallavi Rao, InMobi
10:45–11:05
Timely Dataflow
Bharani S., ThoughtWorks
11:05–11:35
Morning tea break
11:35–12:35
Keynote: Reasoning – The Next Frontier in Data Science
Shailesh Kumar, ThirdLeap
12:35–12:55
Lessons Learned : Real-life NLP
Martin Andrews, Cambridge Business Solutions
12:35–12:55
Looking under the hood - demystifying data tools
Simrat Hanspal, Mad Street Den
12:55–14:10
Lunch
14:10–14:55
Continuous online learning for classification tasks
Saurabh Arora, Freshdesk
14:10–14:55
Scalable Realtime Analytics using Druid
Nishant Bangarwa, HortonWorks
14:55–15:40
Convolutional Neural Networks from the Other Side
Sumod Mohan, SolitonTech
14:55–15:25
Dr. Elephant - Self-Serve Performance Tuning for Hadoop and Spark
Akshay Rai, LinkedIn
15:40–16:20
Evening tea break
16:20–17:05
Model Visualisation
Amit Kapoor, NarrativeViz
16:20–16:45
Hadoop & Cloud Storage: Object Store Integration in Production
Rajesh Balamohan, HortonWorks
16:20–16:55
Birds of Feather (BoF): From Machine Learning to Deep Learning – when, how and why do you make the transition?
Facilitators: Sumod Mohan, Martin Andrews, Prasanna S, Shailesh Kumar
16:45–17:10
Machine Learning the Walmart Way with a Deep Dive into Automated Forecasting System
Anindya Sankar Dey, @WalmartLabs
17:05–17:50
Real Time Fulfilment Planning at Flipkart Scale
Jagadeesh Huliyar, Flipkart
17:10–17:30
Logging at scale using Graylog - Billion+ messages, 100K req/sec
Rohit Gupta
17:30–17:55
Reducing the world with JavaScript
Aruna S, Mapbox
17:55–18:15
Feedback and theme for 2017
Auditorium 1
Auditorium 2
Auditorium 3
Room 1
Hall
C5 (Seminar Hall)
Training Room
08:45–09:30
Check-in and Breakfast
09:30–11:00
Advanced Deep Learning Workshop – Hands-on
Martin Andrews
09:30–11:00
Introduction to Statistics and Basics of Mathematics for Data Science - the hacker's way
Bargava Subramanian
11:00–11:15
Tea Break
11:15–12:45
Contd. Advanced Deep Learning Workshop – Hands-on
11:15–12:45
Contd. Introduction to Statistics and Basics of Mathematics for Data Science - the hacker's way
12:45–13:45
Lunch
13:45–15:30
Contd. Advanced Deep Learning Workshop – Hands-on
13:45–15:30
Contd. Introduction to Statistics and Basics of Mathematics for Data Science - the hacker's way
15:30–15:45
Tea Break
15:45–17:00
Contd. Advanced Deep Learning Workshop – Hands-on
15:45–17:00
Contd. Introduction to Statistics and Basics of Mathematics for Data Science - the hacker's way
Auditorium 1
Auditorium 2
Auditorium 3
Room 1
Hall
C5 (Seminar Hall)
Training Room
08:15–09:00
Check-in and Breakfast
09:00–11:00
Deep Learning for Computer Vision
Hari C M
11:00–11:15
Tea Break
11:15–13:15
Contd. Deep Learning for Computer Vision
13:15–14:15
Lunch
14:15–16:00
Building a scalable Data Science Platform ( Luigi, Apache Spark, Pandas, Flask)
Nischal HP
16:00–16:15
Tea Break
16:15–18:00
Contd. Building a scalable Data Science Platform ( Luigi, Apache Spark, Pandas, Flask)
Jul 2016
25 Mon
26 Tue
27 Wed
28 Thu 08:30 AM – 06:25 PM IST
29 Fri 08:30 AM – 06:15 PM IST
30 Sat 08:45 AM – 05:00 PM IST
31 Sun 08:15 AM – 06:00 PM IST
Hosted by
Bharani S., ThoughtWorks
Jul 29, 2016, 10:45 AM–11:05 AM
Auditorium 2, NIMHANS Convention Centre
View submission for this session
What are the challenges faced in steram processing: Imagine a system where the data is continuously updated and you need to support both historical data + recent stream and avoid the costly recomputation
How does timely dataflow fit in the stream processing model: Will be covering what timely dataflow offers - cyclic computation, notification mechanism, concept of time in stream processing
Why is it different from other stream processing systems like spark/storm/flink : Not all computation can be easily expressed in Directed Acyclic Graphs which most of the stream processing systems offers - one such example is cyclic computation which can be elegantly modelled in timely dataflow
Pros & Cons: Will take a practical example of an aggregation and showcase pros & cons of the timely dataflow model , with code and time taken
I am a passionate developer and a speaker. I regularly speak in the monthly geeknight meetup in chennai and have spoken in GIDS 2014,2015 both the years on dealing with systems that handle large volume of data with unique challenges of near real time processing. I have built and maintained systems for Banking, Media, and Retail domain. I continuously challenge the status quo and constantly thrive to improve on the solutions i have built in the past. This journey has made me build & rebuild real time analytics solutions that crunches large volume of data carefully balancing throughput & low latency