The Fifth Elephant 2024 Annual Conference (12th &13th July)
Maximising the Potential of Data — Discussions around data science, machine learning & AI
Jul 2024
8 Mon
9 Tue
10 Wed
11 Thu
12 Fri
13 Sat 09:00 AM – 06:05 PM IST
14 Sun
Maximising the Potential of Data — Discussions around data science, machine learning & AI
Jul 2024
8 Mon
9 Tue
10 Wed
11 Thu
12 Fri
13 Sat 09:00 AM – 06:05 PM IST
14 Sun
LLMs and GenAI track (Auditorium on the ground floor)
Data engineering and infrastructure track (Seminar halls on first floor)
Terrace (third floor)
Board room (second floor)
Workshop; BOFs for LLMs and GenAI track (Library on the second floor)
Data engineering and infrastructure BOF area (Ground floor foyer)
09:00–09:35
Check-in and registrations
09:35–09:45
Introduction to GenAI and LLMs track; how to make the most of it
09:35–09:45
Introduction to the Data Engineering and Infrastructure track - how to make the most of it
09:45–10:20
Power up LLMs to be Factual Ninjas using GraphRAGs
Siddhant Agarwal, Developer Relations Lead APAC at Neo4j
09:45–10:20
Data extraction from cloud using Atlassian Lithium Platform
Niraj Mishra, Principal Engineer at Atlassian
10:20–10:30
Transition
10:20–10:30
Transition
10:30–11:05
Multimodal Fusion: Fusing Diverse Data Points for Combating Complex Challenges
Bhumika Makwana, Head - Computer Vision and Analytics at GalaxEye Space
10:30–11:05
Vector databases: a bird's eye view
Aditi Ahuja, Software Engineer - 2 at Couchbase
11:05–11:35
Morning break
11:05–11:35
Morning break
11:05–12:30
Sponsored Round Table - AI and its impact on developer productivity
11:30–12:05
Unfreeze your data markets with privacy-preserving DPI
Gaurav Aggarwal, AI/ML at Jio Platforms Limited
11:35–12:10
Food for thought: scaling delight at Zomato using GenAI
Yuvraj Gagneja, SDE-2 at Zomato
11:35–12:10
From foundation to the future: evolution of Dream11's data platform
Vikas Gite, Principal Engineer at Dream11
11:35–12:20
RAGs to riches: transforming data retrieval with vector search
V Rajkumar
12:05–12:15
Transition
12:10–12:20
Transition
12:10–12:20
Transition
12:15–13:00
Birds of Feather (BOF): Deploying AI in key sectors: robust risk mitigation strategies
Moderators: Khyati Jain and Anwesha Sen; Discussants: Simrat Hanspal, Bargava S, Gaurav Aggarwal, Shadab Siddiqui
12:20–13:00
Establishing Causality in Complex Mental Health Issues using AI
Ayushi Agarwal, Head of Data Science at United We Care
12:20–13:00
Sponsored talk: The winter of our discontent: content moderation systems at scale
Dr. Hari Bhaskar, Google
12:30–13:30
Lunch break
13:00–14:00
Lunch
13:00–14:00
Lunch
13:00–14:00
Lunch
13:30–15:00
Building Data Products to Empower Entrepreneurial Execution
Harsh S
13:40–14:45
Imagining the Future of AI in India
Srinivas Kodali
14:00–14:35
Leveraging Llama.lisp for constructing compiler frameworks
Sasank Chilamkurthy, founder and CEO at Von Neumann AI
14:00–14:35
Starting from scratch: lessons learnt from building high performance parallel distributed compute platforms
Vishnu Vasanth, founder at e6data
14:00–14:35
Opening Up Open Source with Indic Language LLMs
Kiran Chandra Yarlagadda
14:00–15:00
Fetch Data from Data Lakes by Building a Low-Code Data Assistant for Businesses using Code LLMs
Abhijeet Kumar
14:35–14:45
Transition
14:35–14:45
Transition
14:35–14:45
Transition
14:45–15:20
Making JIRA more Agile with AI
Shashank Rao, Senior ML Scientist at Atlassian
14:45–15:20
Unlocking the power of Real Time Feature Stores
Sai
14:45–15:45
Birds of Feather (BOF): Licenses to Level the Playing Field in GenAI
Chaitanya
15:00–15:30
Evening break
15:05–16:05
Enterprise-ready data life cycle: powering AI and analytics at scale
Prateek Mandloi, Mayank Kumar
15:20–15:30
Transition
15:20–15:30
Transition
15:30–16:05
From pair programming to piloting: building independent AI data analysts
Karthik Shashidhar, co-founder at Babbage Insight
15:30–16:05
Apache XTables: translating things across tables
Vinish Reddy, software engineer at Onehouse
15:30–16:30
Expert session - GenAI for Business
Shubha Shedthikere, Kirthiraj Yuvaraj, Venkata Pingali, Sameera Poduri
15:45–16:15
Evening break
16:05–16:25
Evening break
16:05–16:35
Evening break
16:15–16:30
Evening break
16:30–17:00
Break
16:35–16:55
Sponsored talk: Accelerating data insights: building and enhancing relevance with GenAI by Amazon and DataStax
Balaraj Abijhan is client relationship manager at Amazon web services (AWS)
16:35–17:20
Wrap up session Track A: Data Engineering and Infrastructure
16:35–17:55
AMA on Building Apps with LLMs
Soma Dhavala
16:45–17:45
Securing big data environments
Shadab Siddiqui
16:55–17:05
Transition session
17:00–18:00
GenAI demo showcases
17:05–17:25
Wrap up session Track B - GenAI and LLMs in Business
Gaurav Bhalotia, Harshad Saykhedkar, Vikram Srinivasan, Kiran Chandra, Karthik Shashidhar
17:20–18:05
Book Discussion on Dream Machine: A Graphic Novel about AI
Rasagy Sharma, Capital One
Jul 2024
8 Mon
9 Tue
10 Wed
11 Thu
12 Fri
13 Sat 09:00 AM – 06:05 PM IST
14 Sun
Hosted by
Supported by
Gold Sponsor
Sponsor
Community Partner
Beverage Partner
Sai
Jul 13, 2024, 2:45 PM–3:20 PM
Data engineering and infrastructure track (Seminar halls on first floor), Bangalore International Centre
View submission for this session
In today’s data-centric world, businesses rely on personalization now more than ever. Whether it’s personalizing user experiences, optimizing operations, or predicting market trends, data plays a pivotal role. To harness the full potential of data, organizations are turning to real-time feature stores. In this talk, we’ll explore what real-time feature stores are, why they matter, and how we at Zupee have leveraged real-time feature stores for various use cases.
Zupee is a rapidly growing real money gaming platform that caters to a growing user base of more than 100 million registered users. We have been building data powered models to enhance the overall user experience and improve the integrity solutions on our app. These include multiple ML models to improve user recommendations, system integrity solutions etc. The full potential of these models can be leveraged when deployed on real-time data. Serving features on a reliable, real-time and centralized basis would be necessary for these to work. This calls for a Real Time Feature store to be built.
By leveraging the real-time feature store, we are able to activate multiple ML use cases such as fraud detection, player protection, personalized experiences, recommender engines etc. on a real-time basis and also serve these centralized features for other use cases such as: