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
Thursday 25 July 2019
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
10:20–11:35
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
Friday 26 July 2019
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
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
12:25–13:25
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
15:00–16:00
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
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