Jul 2019
22 Mon
23 Tue
24 Wed
25 Thu 09:15 AM – 05:45 PM IST
26 Fri 09:20 AM – 05:30 PM IST
27 Sat
28 Sun
Jul 2019
22 Mon
23 Tue
24 Wed
25 Thu 09:15 AM – 05:45 PM IST
26 Fri 09:20 AM – 05:30 PM IST
27 Sat
28 Sun
Auditorium 1
Auditorium 2
Auditorium 3
Birds of Feather (BOF) area
09:15–09:30
Introduction to The Fifth Elephant 2019
09:15–09:25
Introduction to sessions in Audi 2
09:25–10:10
ADAM - Bootstrapping a deep NN-based sequence labeling model with minimal labeling
Shashank Jaiswal, Data scientist at Clustr
09:30–10:10
Solving the vehicle routing problem for optimizing shipment delivery
Venkateshan, Data Scientist at Logistics and Insight team at Flipkart
10:10–10:55
Ghostbusters: optimizing debt collections with survival models
Fasih Khatib, Data Scientist at Simpl
10:10–11:40
Tutorial on core concepts in Social Network Analysis (SNA)
Sandeep Khurana, Data Scientist
10:20–11:35
Invite-only round table on "Ground truths and platform engineering" by Flipkart's Fulfilment and Services Group (FSG)
10:55–11:25
Morning beverage break
11:25–12:00
10 steps to build your own data pipeline from day one of your startup
Kumar Puspesh, CTO and co-founder at Moonfrog
11:35–12:05
Morning beverage break
11:40–12:10
Morning beverage break
12:00–12:40
Sponsored talk: Age of AI Ops
Nitin Gupta, AppDynamics Data Platform lead
12:05–13:20
Invite-only round table on "Ground truths and platform engineering" by Flipkart's Fulfillment and Supply Group (FSG)
12:10–12:55
Taking deep learning to production with RedisAI
Sherin Thomas, Senior software architect at Tensorwerk
12:40–13:20
Anatomy of a production ML feature engineering platform
Venkata Pingali, CEO and co-founder of Scribble Data
12:55–13:55
Lunch break
13:20–14:20
Lunch break
13:20–14:00
Lunch break
13:55–15:25
Tutorial: Taking deep learning to production with RedisAI
Sherin Thomas, Senior software architect at Tensorwerk
14:00–15:00
Birds of Feather (BOF) session: Tackling the complex inter-dependent challenges in transport planning and assignment
Venkateshan Kannan, Flipkart
14:20–14:35
Joint Q&A on data pipelines, operations and scaling challenges
Kumar Puspesh, Nitin Gupta and Venkata Pingali
14:35–15:20
The Anaconda journey: challenges faced in building an OSS business with data
Peter Wang, Co-founder of Anaconda, Inc
15:00–16:00
Birds of a Feather (BOF) session: Creating a data-driven culture in the startup ecosystem
Akash Khandelwal, Flipkart
15:20–16:00
The final stage of grief (about bad data) is acceptance
Chris Stucchio, Head of data science at Simpl
15:25–15:55
Evening beverage break
15:55–16:55
Birds of Feather (BOF) session: Building fulfillment platforms -- India's eCommerce landscape
Govind Pandey; members of Flipkart's FSG team
16:00–16:15
Joint Q&A on bad data and failures in data management
Peter Wang and Chris Stucchio
16:00–16:30
Evening beverage break
16:15–16:45
Evening beverage break
16:30–17:30
Birds of Feather (BOF) session: On ML platforms
Venkata Pingali, ScribbleData
16:45–17:15
Contracts, schema evolution and data pipelines
Agam Jain, Technology Architect at Zapr
16:55–17:30
Flash talks: by participants
17:15–17:45
Analysing high throughput data in real-time
Namit Mahuvakar
Auditorium 1
Auditorium 2
Auditorium 3
Birds of Feather (BOF) area
09:20–09:30
Introduction to Day 2; recap of Day 1
09:20–09:30
Introduction to sessions in Audi 2
09:30–10:15
Leveraging the power of analytics for MarTech
Jacob Joseph, Data Scientist at Clevertap
09:30–10:10
Similarity search for product matching at Semantics3
Abishek Bhat, Member of data science team at Semantics3
10:10–10:40
Improving product discovery via hierarchical recommendations
Neha Kumari, Software developer with Recommendation team at Flipkart
10:15–10:55
Demystifying Social Network Analysis (SNA)
Sandeep Khurana, Data Scientist
10:15–10:55
BoF on ML and Kubernetes
Krishna Durai, Ravishanker KS
10:40–11:10
Morning beverage break
10:55–11:25
Morning beverage break
10:55–11:25
Morning beverage break
11:10–13:10
Tutorial: Meet TransmogrifAI, Open Source AutoML powering Salesforce Einstein
Rajdeep Dua, Salesforce
11:25–12:05
Technology to counter misinformation/disinformation
Pratik Sinha, Co-founder at Alt News
11:25–12:25
Challenges and approaches for instrumenting and cleaning 'real'/ ugly data
Kranthi Mitra
11:45–12:30
Birds of a Feather: ML in production
Simrat Hanspal
12:05–12:40
Sponsored talk: Feed generation at ShareChat
Ayush Mittal, Lead data scientist at ShareChat
12:25–13:25
Birds of Feather (BOF) session: Controlling narratives on Twitter
Pratik Sinha, Sandeep Khurana
12:30–13:15
Birds of Feather (BOF) session: On data science and its applications in agriculture
Karnam Vasudeva Rao
12:40–13:05
Data security and startups: make the ends meet
Shadab Siddiqui, Head of Information Security at Hotstar
13:05–14:00
Lunch break
13:10–14:00
Lunch break
13:25–14:00
Lunch break
14:00–14:20
Why data privacy is critical for robust data management?
Peter Wang, Co-founder of Anaconda, Inc
14:00–14:40
How GO-FOOD built a query semantics engine to help you find food faster
Ishita Mathur, Data scientist at GO-JEK
14:00–15:05
Birds of Feather (BOF) session: On multi-tenancy in ML -- the SaaS perspective
Swaminathan Padmanabhan, Director of Data Science initiatives at Freshworks
14:00–15:00
Birds of Feather (BOF) session: On Interpretability of ML Models
Namrata Hanspal
14:20–14:45
How to build blazingly fast distributed computing like Apache Spark In-house?
Upendra Singh, Full stack data scientist at Clustr
14:40–15:00
Demo session: Samantar: an open assistive translation framework for Indic Language
Deepthi Chand
14:45–15:15
A journey through Cosmos to understand users
Avinash Ramakanth, Tech lead at InMobi
15:00–16:15
Demo sessions: share your idea; get feedback
15:00–16:00
Birds of Feather (BOF) session: Anomaly detection at large scale (for data security)
Shadab Siddiqui, Bargava Subramanian
15:10–16:15
Birds of Feather (BOF) session: On intent classification and personalization
Ramanan Balakrishnan, Semantics3
15:15–15:20
Flash talk on MUDPIPE: malicious URL detection for phishing identification and prevention
Arjun BM, Security architect at Target
15:20–15:45
Flash talks by the audience
15:45–16:15
Evening beverage break
16:00–16:45
Unpacking the Learning Paradigms
Amit Kapoor
16:15–16:55
How we built a ML model to predict proteins for insecticidal activity?
Karnam Vasudeva Rao, Senior data scientist at Bayer
16:15–16:45
Evening beverage break
16:15–16:45
Evening beverage break
16:45–17:30
Birds of Feather (BOF) session: On ML Model Management
Ravi Ranjan
Jul 2019
22 Mon
23 Tue
24 Wed
25 Thu 09:15 AM – 05:45 PM IST
26 Fri 09:20 AM – 05:30 PM IST
27 Sat
28 Sun
Hosted by
Sherin Thomas, Senior software architect at Tensorwerk
Jul 25, 2019, 1:55 PM–3:25 PM
Auditorium 2, NIMHANS Convention Centre
View submission for this session
Year 2018 was the year of model servers. There were numeroius initiatives for building a reliable, interoperable deep learning deployment toolkits but so far we don’t have an easy tool that can reliably handle the deep learning models from all the frameworks. With the advent of Redis modules and the availability of C APIs for the major deep learning frameworks, it is now possible to turn Redis into a reliable runtime for deep learning workloads, providing a simple solution for a model serving microservice. In this talk we will introduce RedisAI, a joint effort by [tensor]werk and RedisLabs that introduces tensors and graphs as new Redis data types and allows to execute graphs over tensors using multiple backends (PyTorch, TensorFlow, and ONNXRuntime), both on the CPU and GPU. The module also supports scripting with TorchScript, which provides a Python-like tensor language that can be used to facilitate pre- and post-processing operations, like input shaping or output ensembling. In addition, thanks to its support for the ONNX standard, including ONNX-ML, RedisAI is not strictly limited to deep learning, but it offers support for general machine learning algorithms. In this talk, we will demonstrate a full journey from training a model to deploying to production in a highly available environment. Last, we will lay down the roadmap for the future, like automated batching, sharding, integration with Redis data types (e.g. streams) and advanced monitoring. The talk will include sample code, best practices and a live demo.
I am working as a part of the development team of tensorwerk, an infrastructure development company focusing on deep learning deployment problems. I and my team focus on building open source tools for setting up a seamless deep learning workflow. I have been programming since 2012 and started using python since 2014 and moved to deep learning in 2015. I am an open source enthusiast and I spend most of my research time on improving interpretability of AI models using TuringNetwork. I am part of the core development team of Hangar and RedisAI and a constant contributor to PyTorch source. I also have authored a deep learning book. I go by hhsecond on internet