X Topic Abstract

Artificial Intelligence and Machine Learning provide faster, more accurate assessment with enormous data processing powers, and consider a wide variety of factors which leads to better-informed and data-backed decisions. AI based outcomes are based on complex algorithms and sophisticated rules to help clients streamline and optimize processes ranging from digital adoption to personalized banking, fraud management, intelligent automation and NLP based customer satisfaction analysis.

Speaker Profile

Manisha Banthia has over 23 years of experience in Analytics in Financial Services and currently heads the Analytics CoE at Fiserv Global Services.

She has earlier worked in Infosys consulting team heading their analytics team and was instrumental in developing building analytics products, including a patent for ‘Customer Analytics for Enterprises’.During her stints at Oracle Financial Services Software (earlier iflex solutions) she built analytics solutions for Citibank (Asia Pacific), and US clients like American Stock Exchange and Country Financial.

Manisha has also worked on Marketing Analytics, Financial Performance and Fraud and Risk Analytics during her tenure at MetricStream and National Stock Exchange of India.

Manisha is based out of Bangalore and heads analytics teams in India, Costa Rica and US for Fiserv.

X Topic Abstract

1: Introduction to machine learning recommendation system
2: Use case of Netflix movie recommendation system

Speaker Profile

Rohit Garg started his career with Citibank Corporate Banking. In his current role as Vice President, at RRD Go Creative, he manages a number of large Financial Services teams in Asia. He has been speaking at a number of automation conferences and sharing his views on the impact of automation on the financial services industry. Rohit is a Graduate from NIT Allahabad and also holds Master degree from NITIE, Mumbai.

X Topic Abstract

In e-commerce , customer experience can be divided into broadly 3 parts - pre purchase, purchase and post purchase. Drivers of good customer experience vary across these 3 parts of the customer journey. At Myntra , we heavily use data based insights and advanced ML models to identify what product features, merchandise selection and service propositions optimise customer experience while helping the business meet it's financial goals. Some examples include search personalisation, catalogue optimisation using computer vision, automated return approval for "trusted" customers etc. The presentation would cover some examples of projects that have been undertaken at Myntra and also talk about what metrics should be tracked to ensure your company is meeting the customer experience goals.

X Topic Abstract

Data science and Machine learning (DS & ML) platforms are now quite popular and there are more than a dozen good platforms available in the market from reputed vendors. What are DS & ML platforms and what are the advantages of having one in your enterprise. How do you decide whether you need one? Do you need it now or should you wait? What are the capabilities that you should be looking for in your Data science platform?. Learn in this session

Speaker Profile

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).

X Topic Abstract

In this increasingly competitive world, delivering personalized customer interaction using machine learning has become key to a business's success. Products are the same and available everywhere. But how do you make the experience of finding the right products easy and fast for your customers? How do you keep them engaged and not lose to the competitor is a challenge?. These are bothering questions that business needs to address. Personalization ensures that your Products & offers are relevant. This is not just true to e-commerce sites but to enterprise applications and simple consumer apps too.

Gartner also predicts that smart personalization engines that recognise customer intent will enable businesses to increase profits by up to 15%. 74% of consumers get frustrated with the content that has nothing to do with them.

This presentation will explore different design strategies on how to build smart personalization and increase satisfaction with more revenue/productivity.

Speaker Profile

Sameer has over 23 yrs of design and leadership experience at leading product companies building desktop, web, mobile, wearable & Speech applications. He holds a Master’s degree in Industrial Design from IIT Mumbai and a Bachelor's degree in Mechanical Engineering. He has published papers, UX patents and has been a speaker at many conferences and colleges. Lastly he was the Head of Design at Flipkart.

He has played multiple roles ranging from VP of Design to being a design consultant and entrepreneur. He has worked as VP of Products at Spire Technology, Director Experience Design at Intuit & Human factors Lead at Intel California. Prior to this, he was Chief Researcher for LG R&D Lab in Seoul, South Korea. Sameer has also worked at enterprise software companies across India namely Oracle in Hyderabad and Siemens in Pune. He nurtured his entrepreneurial skills as VP of Design for Naukri.com in Noida. Early in his career, Sameer was an Industrial designer working for Piaggio scooter in Pontedera, Italy and as lead engineer in Mahindra & Bajaj Auto

X Topic Abstract

How is TVS motors deploying AI and ML enabled solutions to improve customer experience

Speaker Profile

Anand is part of senior leadership team at TVS motors. He leads the data engineering, data science & BI capabilities for customer facing functions like sales, marketing, distribution network, Digital, parts, services etc. In his current role, his goal is to help end-customers get a world class TVSM experience, help channel partners become profitable and help TVS drive sales growth and market share. He intends to do this by enabling faster and better decision making with actionable AI and world-class data management.

X Topic Abstract

Most of them think that AI/ML is a complete black box and only accuracies matter, which is not the case in industry level scenarios or any Machine Learning problem statements.

Interpretation of ML models is highly important and how do we interpret that and tweak it to get industry level stats is the thing which will be covered in this session

X Topic Abstract

Time series is defined as a set of random variables that are ordered with respect to time according to descriptive statistics. It is used to interpret a phenomenon, trend analysis and predict the future value. Examples include Stock price analysis and forecasting, Cash flow analysis, Supply chain management, production, inventory planning and budgeting. Predictive models are developed through machine learning algorithm is implemented in time series dataset for facilitating predictive distribution of time and resources. Time series as defined earlier specifies the sequence of observations collected in an instant of time interval daily, monthly, quarterly or yearly. Time series Analysis develops models to describe the observed time series dataset and understand the same through assumptions and interpretations. Time series forecasting uses best fitting models to predict the future observation based on complex processing of current and previous data. Machine learning proved to be the most effective in capturing the patterns in the sequence of both structured and unstructured data and its further analysis for accurate predictions. In this topic I will project on the case study applied to predicting the future sale using kaggle dataset. The focus will be on the various types classical and neural network based methods among which how to determine the best model used in time series analysis. Further will focus on the Time Series forecasting process which involves

1. Project goal definition
2. Data gathering and exploration
3. Data preparation
4. Applying time series forecasting method
5. Evaluation/Validation and performance comparison
6. Deployment

These time series data forecasting involves challenges like Lack of data and Lack of domain knowledge and how to overcome the same will also be explained in detail. Major focus of the presentation will deal with Covid19 pandemic analysis and predict the future spread of the disease using machine learning approach in time series analysis.

X Topic Abstract

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

X Topic Abstract

In this talk, we will cover the basic of Deep Learning and how to implement it hands on for text, images and videos. We will take 3 problems in Machine Learning for text, images and video and build classifier using Deep Learning. We will talk about different architecture like LSTM, CNN for treating images and video. The key take away from this talk is to get the basic architecture of Deep Learning and how to implement them in pytorch to do basic text or image classification.

Speaker Profile

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.

X Topic Abstract

Service Desks requires good strategy and innovation to improve customer service and to support business goals. Cisco TAC is constantly looking at ways to provide customers with the information they need via a self-service capability in an attempt to reduce the number of support requests that are opened. This is frequently referred to as case avoidance. A significant amount of information is lost while exchanging mail, chats or over case notes.

The impediment to progress is the inability to derive actionable insights from this unstructured text since most of the prioritize corrective action is buried in these notes. Using advanced ML techniques along with text mining solution, the objective is to extract actionable insights for TAC that can be used to drive down Service Request volume. The proposed solution comprises of text parser and automated case deflection Conversational NLP solution which can provide an immediate solution using historic data analysis"

Speaker Profile

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.

X Topic Abstract

NLP (Natural Language Processing) is the new buzzword that has set the AI / ML world into fire. In actuals, NLP is evolving into a new branch of AI altogether.

So here, let's discuss and have hands on the core of NLP - which is nothing but - TEXT CLASSIFICATION.

Let's set the stage:
Working on a ML problem is like cooking .
Yeah while cooking, one has to:
Decide the dish and recipe,
Source the ingredients,
Cook them and present them beautifully.

On the same lines, the agenda will be:

Prep work:
A refresher on ML concepts and NLP
Understand about TEXT Classification and its importance which can brings in billions of revenue
· Decide the dish & Recipe
· Here we will be setting up the problem

Setting the environment
Explanation of the same

Source the ingredients:
Learn the data set
Load the dataset
Let's start cooking:
Coding and solving the issue / problem in hand Includes modeling, evaluating the model etc Serve the DISH
· Do the prediction and analyze the results

Take away:

The participants will get to know on:
Why TEXT classification is so important (Tech giants like Google are making billions out of it)
Learn about important concepts like & various techniques like:
Bag of words
Vectorization
TF-IDF (used by Google for displaying search results)
Pipelines
How to choose the best classifier
What is performance tuning

See in action algorithms like:
§ SVM
§ Naïve Bayes
§ Performance tuning with Grid search

Compare the performance with the super-efficient NLTK library

Speaker Profile

Bharathi (Principal Engineer, Unisys India) is best identified as tech enabler in 4G and 5G space who is 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.

X Topic Abstract

NLP based virtual agents are servicing robo advisory, be it explanation of financial products or of health plan benefits. The use cases will discuss on importance of interplay of ML and conventional engineering to solve end user problems, and explore an NLG approach to tackle the data non-availability issue for training purposes.