How we are leveraging user and customer first principles and building out self-serve data products and data foundations to deploy effective data analytics for business decisions.

Anand leads the data engineering, data science & BI capabilities for customer facing functions of TVS motors like sales, marketing, distribution network, Digital products, parts, services and international business.

In his current role, his goal is to help end-customers get a world class TVSM experience and help business stakeholders drive key business KPIs, by enabling faster and better decision making with actionable AI and world-class data management.

ML is rapidly getting adopted to solve critical Technical & Business Problems in the Industry. For example in the Semi-Conductor Industry availability & cost of Computing resources are key issues for Design Simulation & Verification using EDA tools. This is becoming more pronounced as Design Complexity increases with 3nm, 5nm & 7nm design Techniques. Also, Semi Companies do multiple chip designs simultaneously & which adds to the crunch of computing resources & can add to several weeks of delay. ML-based Orchestration Engine has helped to solve this problem. Similarly in the Manufacturing Industry using intelligent ML software, supply chain managers can optimize inventory and find the most suited suppliers to keep their business running efficiently.

My talk will focus on my work in niche microinsurance based product offerings which are tailor made for a focussed audience group, such as: - Easy-to-purchase flight delay insurance using blockchain architecture (ref. fizzy.axa) focussing on millenials and frequent flyer groups - Usage based motor insurance as per the driving behaviour & risk score of the user - Parametric event interruption insurance (such as rains during cricket match etc.) I shall specifically stress on how are we building active machine learning workflows in designing the experience these products cater to their users while also touching upon the architecture which makes it feasible for implementation and monitoring production in the long run.

Amitanshu heads the New Products and Business Initiatives team at Bharti Axa General Insurance and has a diverse experience in speaking at various data science keynotes for corporates and student groups alike.

After completing his bachelors in Mathematics and Scientific Computing from Indian Institute of Technology, Kanpur (IITK), Amitanshu has extensive experience in both academia and corporates – where he worked across verticals such as Telecommunications, Retail, E-Commerce and Insurance.

In his current role, Amitanshu leads the development of innovative micro-insurance product portfolios encompassing delays (flight delay insurance, cab trip delay insurance etc.), consumer durables & electronics such as gadget insurance, bicycle insurance etc. offering seamless customer claims experience with minimal customer involvement or paperwork.

Prior to this, Amitanshu has lead the end-to-end development & deployment of various Data Products leveraging sophisticated Machine Learning Algorithms on various fronts viz. Chatbots, Voicebots, Recommender Systems etc.

On a typical work day, you can find Amitanshu busy in strategizing optimal insurance cross-sell/up-sell strategies along with hands-on problem solving with Data Science & Product teams while implementing robust data architectures for optimum digital-first insurance product offerings. Apart from work, Amitanshu is a professional guitarist, with a passion for classic rock bands such as Pink Floyd, Led Zeppelin, Radiohead, etc. and an avid adventure sports enthusiast.

I will be picking up use cases for ML application in Credit Risk Management ( Trade matching automation), Data Mining for third party data vendor files, Data matching using Elastic search for Third party data, Use cases of Fraud Risk Management and potential opportunities for bankers to upscale from near time detection to real time detection

30 minutes Presentation with real life examples - I will be discussing both real life examples and other use cases of AI/ML in banking.

Key takeaways from this session:

This talk would cover American Express’ exciting journey to explore AI/deep learning technique to generate next set of data innovations by deriving intelligence from the data within its global, integrated network. Learn how using credit card data has helped improve fraud decisions elevate the payment experience of millions of Card Members across the globe.

Featured among Top 10 Data Scientists in India, Manish Gupta is Vice President in American Express, he leads machine learning and data science team that builds state-of-the-art machine learning solutions and leverages them in risk and analytics decisions across the globe. He is also center head of Credit & Fraud Risk CoE Bangalore.

Manish Gupta has extensive experience in successfully leading enterprise wide AI, machine learning and data science practices across diverse industries such as Internet/E-commerce, Banking, BPO and Defence. He has also served the country as a Scientist at DRDO and holds PhD degree from IIT Delhi in the area of Machine Learning with 15+ research papers and 1 US Patent which have 400+ citations.

NLP and Image processing using Deep Learning has achieved leaps and bound. But can we trust them? How about breaking Deep Learning models? How do you know the model you trained is not subject to adversaries in data or in practice? In this talk, I will cover different techniques to test your model robust ness to the extreme and simple methods to overcome problem of training highly ovedfitted models which might lead to very erroneous output.

I am a deep learning researcher, after graduating from Indian Statistical Institute with Master's in Computer Science, with specialization in NLP and IR, I have worked with Bing Ads, Microsoft R&D and HSBC Bank as decision scientist to build risk models for bank. I am currently working in Amazon as a data scientist to build models to cater to amazon internal network.

How AI can be used to Save Food Waste.
ML Strategy for ML Model
Data Strategy for Various Food
ML data Strategy to detect, track, and update food status
help the consumer by AI-powered informationn

Prashant Bhat is a Thought Leader in AI/ML Data Science Digital Transformation. As a Global COE Head of Data Science, Machine Learning and Artificial Intelligence instrumenting combine strategies, collaboration, analytics, and technical proficiency to launch successful AI/ML models. working with technologies that deliver results in the disciplines of Data Science, Data wrangling, Machine Learning, software engineering, data visualization, ML in Production. Along with AI/ML, he is leading AR/VR/MR or XR technology too. His Strategic Data Science Road Map has been apricated a lot. He has worked for esteem organizations like Whirlpool, Ulatrons (founder), MSC, HoneyWell, GE. Carrying two decades in software and product development, working on the landing prediction, F1 Turbo Analysis, AI-Powered Smart Appliance, tools for object tracking and count, End to End ML Pipelines, etc.

Ever wondered & got stuck on how to do enterprise planning by having ML & AI. Then this session is for you.One will get to know on:
What is ML & AI based Enterprise Planning?
Planning Goals
AI Maturity model
Discovery maps
AI Accelerators
AI Based Enterprise roadmap
AI Application Use cases

The use cases will cover broad range including:
Predictive maintenance
Sales forecasting
Conversational AI with NLP
Churn prediction

“Bharathi (Principal Engineer, Unisys India) is best identified as tech enabler in 4G and 5G space who is also a pioneer in AI field and also in RPA.
He is a PYTHOnator by heart who like to transform the industry for greater good. Under his mentorship, hundreds of people (from students community and also from the industry) got into the bandwagon of Python and automation. He is holder of THREE nanodegrees (Two in AI and one in Front end web development). He loves to develop Alexa based skills as his hobby and ready to embrace new challenges every day.

Improving the efficiency of the supply chain plays a crucial role for any enterprise. Operating their businesses within tough profit margins, any kind of process improvements can have a great impact on the bottom line profit. Most accurate Demand forecasting is especially important for all industries because it leads to lower inventory costs, faster cash turnover cycles, quicker response to trends, and better margins

Automated AI & ML driven demand planning and optimization solutions can forecast future demand and generate the stock replenishment requirement dynamically and add value to the business (for example Dynamic stock replenishment).

In this session we are going to discuss how Natural Language Processing enables machines to understand human language.

We also discuss the different Natural Language Processing Services and how it helps us to build applications to find insights and relationships hidden inside the language.

We will also go through the rapid innovations happening in the Machine Learning and Deep Learning world to perform Natural Language Processing more efficiently.

Suman Banerjee is a Global Enterprise Solution Architect at Amazon Internet Services Pvt Ltd. A Builder at heart with more than 20 years of experience in partnering with client leadership team, framing Cloud Architecture Strategies, Enterprise Architecture Strategies, Digital Strategies, Integration Strategies and Technology roadmaps and in delivering IT enabled business transformation projects.

Enterprises are now seeing value and investing in Data Science and Machine Learning projects. While many are able to successfully build models that seem to solve their business problems, very few model deployments are making it to production and able to sustain the model performance where the business applications can continuously leverage the insights. Why are the success metrics low for model deployment and operationalisation in production? What are the common challenges?

Are your Data scientists spending more time on operationalising their models and related life cycle activities in in production than really building and tuning models? What can you do to improve this ratio? Learn in this session

I plan to share those and practical insights and solutions especially around ML and DL models in enterprise production. Also the various trends in this field and what practitioners would have to be cognizant of.

Vinod Khader has around 20 years experience in Software Development and is an Associate Director at IBM Software Labs in the Data and AI division. In the current role, he leads the development of Watson Machine Learning Platform on IBM's public and private cloud platforms with teams across the globe. Watson Machine Learning platform help Data Scientists, Data Engineers and App Developers in managing the end to end Machine Learning and Model management life cycle starting from Training, Evaluation, Deployment and Scoring (prediction).

PayPal has a huge Credit portfolio, offering revolving credit and instalment options to consumers across the world – US, UK, France, Germany, etc. The decision to offer credit to a customer is made in a few milliseconds in real-time. This is powered by PayPal’s state-of-the-art deep learning algorithms and the rich data we have about consumer preferences. This presentation will provide insights in to the these algorithms and data with real examples.

Venkat Subramanian heads the Credit Data Science team in India as part of the Global Data Science group within PayPal. His team focuses on supporting PayPal’s credit business across markets with predictive machine learning models.

Venkat has overall 13 years of experience spanning Data Science, Corporate Strategy and Financial Technology. His prior stints include leading the Pricing Data Science team in Chennai for Maersk (global shipping conglomerate), and being part of the quantitative trading technology team for D E Shaw (hedge fund based in New York).

He graduated with MBA from IIM Bangalore and BE (Comp Sci) from College of Engineering Guindy, Anna University, Chennai. He also holds a CFA certification from the Global CFA Institute in the US.

Automated vehicles of the near future will need more than cameras and radar to operate effectively and safely – they will need a brain. Just as humans rely on thought processes to coordinate and direct their arms, legs, eyes and ears, autonomous vehicles need to be able to think – not just indiscriminately respond to sensor stimuli. Embedded vision solutions will be a key enabler for making automobiles fully autonomous. Giving an automobile a set of eyes – in the form of multiple cameras and image sensors – is a first step, but it also will be critical for the automobile to interpret content from those images and react accordingly. Object detection no longer has to be a hand-crafted coding exercise. Deep neural networks, such as CNNs, allow features to be learned automatically from training examples. A two-step process involving RoI (Region of Interest) followed by CNN-based object detection can be used for efficient & accurate prediction of vehicle course

Aashish Bhatia is the President of visteon India. He is responsible for strategizing and leading a profitable growth for Visteon India. In this role, he is also responsible to drive productivity and capability of product development team of Visteon Corporation. Aashish has over 25 year of experience in product engineering, automative electronic design, program management, sales and business development, and enterprise-wide leadership. He has a Bachelors degree in Electronics Engineering, a Masters degree in Design Engineering from IIT Delhi, and has successfully completed an executive management program form IIM Bangalore. Soon after his masters, he joined GE Aircraft Engines in research and development. He embarked upon his automative electronics career at Delhi and later held various senior management position at Hella Automative and Inteva products, Before joining Visteon in 2019. Aashish has published 9 technical papers; He is a life member of various professional organizations and has secured various honors and awards during his professional career.

1. What is network data and how to read syslog.
2. Interpretation of syslog data
3. Text Analytics/ NLP on syslog data
4. Identification of devices which has caused the outage
5. Reduction in TAT (turn around time) for resolution

Vivek is currently a data scientist @Cisco. He has 7+ years of work experience across E-commerce, mortgage, Retails and CPG domains. He is an extensive NLP researcher, he has a pragmatic approach in text mining, NLP, Machine learning and social media analytics. Prior to Cisco, he has also worked across organizations like Xerox, BRIDGEi2i and Altisource.

What makes explainability difficult in AI/ML
How to go about adding explainability and interpreting it
Adding MLI (Machine learning interpretability) to Deep Learning models across text and vision projects
Pros and Cons of adding MLI in AI/ML models

In business areas where there are decisions involved that have social implications and we have seen biases in human decisions or data skewed undesirably towards a particular category, it is important that our ML/AI algorithms are tuned to eliminate any such bias that are bound to creep in automatically due to historical data. How to we identify those scenarios and eliminate biases effectively from our Machine Learning algorithms.