Developing a bot that can answer support queries and aid in decision making with analytics

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

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Accepting submissions till 15 Jun 2019, 01:00 PM

NIMHANS Convention Centre, Bengaluru

Tickets

Pinned update

The Fifth Elephant Winter edition starts at 9:30 am; live stream for members This update is for participants only

##The eighth edition of The Fifth Elephant will be held in Bangalore on 25 and 26 July. A thousand data scientists, ML engineers, data engineers and analysts will gather at the NIMHANS Convention Centre in Bangalore to discuss:

Model management, including data cleaning, instrumentation and productionizing data science.
Bad data and case studies of failure in building data products.
Identifying and handling fraud + data security at scale
Applications of data science in agriculture, media and marketing, supply chain, geo-location, SaaS and e-commerce.
Feature engineering and ML platforms.
What it takes to create data-driven cultures in organizations of different scales.

##Highlights:

1. Meet Peter Wang, co-founder of Anaconda Inc, and learn about why data privacy is the first step towards robust data management; the journey of building Anaconda; and Anaconda in enterprise.
2. Talk to the Fulfillment and Supply Group (FSG) team from Flipkart, and learn about their work with platform engineering where ground truths are the source of data.
3. Attend tutorials on Deep Learning with RedisAI; TransmorgifyAI, Salesforce’s open source AutoML.
4. Discuss interesting problems to solve with data science in agriculture, SaaS perspective on multi-tenancy in Machine Learning (with the Freshworks team), bias in intent classification and recommendations.
5. Meet data science, data engineering and product teams from sponsoring companies to understand how they are handling data and leveraging intelligence from data to solve interesting problems.

##Why you should attend?

Network with peers and practitioners from the data ecosystem
Share approaches to solving expensive problems such as cleanliness of training data, model management and versioning data
Demo your ideas in the demo session
Join Birds of Feather (BOF) sessions to have productive discussions on focussed topics. Or, start your own Birds of Feather (BOF) session.

##Full schedule published here: https://hasgeek.com/fifthelephant/2019/schedule

##Contact details:
For more information about The Fifth Elephant, sponsorships, or any other information call +91-7676332020 or email info@hasgeek.com

#Sponsors:

Sponsorship Deck.
Email sales@hasgeek.com for bulk ticket purchases, and sponsoring 2019 edition of JSFoo:VueDay.

JSFoo:VueDay 2019 sponsors:

#Platinum Sponsor

#Community Sponsors

#Exhibition Sponsors

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#Community Sponsors

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The Fifth Elephant

The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more

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Developing a bot that can answer support queries and aid in decision making with analytics

Submitted Apr 15, 2019

Session type: Discussion Session type: Full talk of 40 mins

Responding to repetitive queries from customers can overload the support team. Developing the capability to handle such repetitive queries can significantly enhance the productivity of support agents and they can utilise their time in resolving problems that are more challenging and involved.
This talk will focus on the modelling approach that we at Freshworks took to develop a bot that has the ability to understand natural language, respond to customer messages and aid agents in understanding the kind of content that needs to be created to enable self service. In addition, we’ll also throw light on deployment and challenges involved in scaling a system such as this.

Outline

Motivation - Why should this problem be solved
Problem Statement - Definition of the end objective
Modelling Methodology - An overview of the available data, ML algorithms and validation metrics that define success
Deployment in Production - An overview of the pipelines for data extraction, training models, serving predictions and feedback consumption
Challenges - scaling and dealing with streaming data

Requirements

No Requirements

Speaker bio

I joined the data science team of Freshworks in May 2018 and since then have been working on problems in the NLP space. Prior to this, I have experience of solving problems including risk modelling, churn modelling, life time customer value prediction, campaign analytics, creating networks from social data to decipher relationships and market mix modelling.