Recommendation @ Scale

Nov 2019

18 Mon

19 Tue

20 Wed

21 Thu

22 Fri

23 Sat 08:30 AM – 05:30 PM IST

24 Sun

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Accepting submissions till 01 Nov 2019, 04:20 PM

Taj M G Road, Bangalore, Bangalore

Tickets

##About the 2019 edition:

The schedule for the 2019 edition is published here: https://hasgeek.com/anthillinside/2019/schedule

The conference has three tracks:

Talks in the main conference hall track
Poster sessions featuring novel ideas and projects in the poster session track
Birds of Feather (BOF) sessions for practitioners who want to use the Anthill Inside forum to discuss:

Myths and realities of labelling datasets for Deep Learning.
Practical experience with using Knowledge Graphs for different use cases.
Interpretability and its application in different contexts; challenges with GDPR and intepreting datasets.
Pros and cons of using custom and open source tooling for AI/DL/ML.

#Who should attend Anthill Inside:

Anthill Inside is a platform for:

Data scientists
AI, DL and ML engineers
Cloud providers
Companies which make tooling for AI, ML and Deep Learning
Companies working with NLP and Computer Vision who want to share their work and learnings with the community

For inquiries about tickets and sponsorships, call Anthill Inside on 7676332020 or write to sales@hasgeek.com

#Sponsors:

Sponsorship slots for Anthill Inside 2019 are open. Click here to view the sponsorship deck.

Anthill Inside 2019 sponsors:

#Bronze Sponsor

#Community Sponsor

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Anthill Inside

Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more

All submissions

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Recommendation @ Scale

Submitted Apr 30, 2019

Section: Crisp talk Technical level: Intermediate Session type: Lecture

Recommendation is one of the most traditional and wide spread use case of Machine Learning. In this talk we want to showcase, how an advanced recommendation engine can be served at scale in Glance. Glance is an AI-powered, content driven, personalised Screen Zero (lockscreen) platform for mobile, which is used by over 26M DAU users in India. The talk will take you through each component of a recommendation engine and in the end will showcase the learnings which we got from our experiments to make it functional at scale.

Outline

What is Glance
Recommendation Engine in Glance
Serving Architecture
Learnings

Speaker bio

Aditya Patel is Director, Data Science at InMobi-Glance. Previously he was head of data science at Stasis and has 7+ years of experience spanning over the fields of Machine Learning and Signal Processing. He graduated with Dual Master’s degree in Biomedical and Electrical Engineering from the University of Southern California. He has presented his work in Machine learning at multiple peer reviewed conference. He also contributed to first generation “Artificial Pancreas” project in Medtronic, Los Angeles. In his current role, he is aiding in building the biggest content platform in India.