Key takeaways from my session:
Most Enterprise AI use cases still fail to scale and to translate into productive solutions
Organizational change in Enterprise IT towards empowering an AI culture can help
Combining the strength of Data Scientist’s experimental approach with Software Engineering’s DevOps methods can help
Building and maintaining a firm AI Reference Architecture can help

Yannick has been working in IT and Data science for over 20 years. From his experience at major companies, software vendors and consulting firms, he has built a broad outlook on solving business problems with innovative technologies. He is convinced that the combined use of Data Science and Machine Learning along with sound software engineering is the new disruptive force for businesses and communities.
After having founded a fintech startup applying data science to combating financial crime, Yannick has joined Capgemini in 2019. He is helping Capgemini's clients to scale AI while basing on sound ethical principles.

The newest industry disruption is the Low-Code/No-Code (LCNC) development aims to empower business users to rapidly develop codeless applications to meet their needs. The distinction between tools that were thought to be simple enough for citizen developers and powerful enough for professional development teams are disappearing. In a true IT democratization process, citizen development is helping accelerate digitization of processes at an unprecedented rate. Through real-world examples, this talk aims to highlight the LCNC trend and how it is helping with Analytics and BI. Talk also provides constructive approaches which help IT / engineering teams embrace citizen developers while eliminating the historical points of friction from “shadow IT” work.

Garima V. Sharma is a senior engineering leader at Microsoft. She is a seasoned Tech professional, keynote speaker, STEM champion, technical blogger and proponent of diversity and inclusion. She runs a global team of data engineers and scientists focussed on building world-class analytics and BI solutions and next-gen AI applications. Garima has a passion for technology and has worked in progressive companies on Cloud Computing, Big Data & Artificial Intelligence solutions. She has helped shape science and technology for mission critical software, reliability in operations and re-design of architecture all geared towards advancements in medicine, security, cloud technologies and bottom line savings for the client businesses. An enthusiastic technology and STEM blogger, she brings together her passion for tech and diversity in her professional and personal endeavors.

We have created Next Best Offer recommendation for users. We have send the offer via App on Mobile phone.
There are various best offers for every client based on their past behavior. There are basically three best offers that every eligible user receive that are relevant for him.

20+years industry experience world-wide (Netherlands, USA, Serbia), from science& engineering (PhD in EE, UTwente NL) to marketing, business, analytics to Applied Data Science. In 2016 he started Data Science & Analytics team within marketing department of Telenor Serbia. He has been a keynote speaker and presenter to many Data Science conferences in Europe; promoting applied data science in Marketing & Business. Currently, he is as a freelance consultant on Business Convenient Analytics & Data Science.

Over the past few years, we have seen increased activity in the development of software powered applications in a range of sectors, such as autonomous vehicles, security, and data mining. As the level of investment and research effort in these fields intensifies, companies are looking to protect the rights to the Intellectual Property of their innovative solutions and algorithms to protect their technology roadmap and gain an edge over their competitors. But what are the different ways we can protect these innovations? In this presentation, we will discuss the protection of software - based innovations, discuss how to deal with A.I “inventors”, and provide examples of IP considerations that need to be considered during the product development lifecycle.

Data Science & Machine Learning | Artificial Intelligence & Big data | Introduction|History|Regulatory Aspect| Application of data science | Tools and evolving capabilities | Data | Uniqueness & challenges | Classification Algorithm

Case Study:

Predicting bank loan default | Data| Machine Learning Algorithms | Results & Discussion.

In this talk I will provide an overview of banks history, its regulatory aspects and application of machine learning in banking with real-time use case.

Deepti Gupta has done her MBA in Finance and PGPM in operation research. She has worked with KPMG and IBM and is currently working as a data scientist in the banking sector . Deepti has extensive experience in predictive modeling and machine learning with an expertise in SAS, R and Python. Deepti has authored a book on data science and machine learning and has developed data science courses, delivered data science training, and conducted workshops for both corporate and academic institutions. She has written

Definition Purpose of Strageic planning
Current AI Use in Marketing and Financial Management
Steps in SP
Data types in SP
Data sources in SP
Datawarehouse vs Data Lake
Problems to be addresses
Our Solutions
A video on Animated description on how we do

AI/Natural Language Processing technologies to analyze and improve voice-based interactions with customers by analyzing conversations at large scale

As Vice President of Engineering at Zillow Group, Andrei leads the teams responsible for the company’s search and discovery engines, including search science, development, infrastructure, and operations. As part of this role, he heads Zillow Group’s conversational AI efforts, which consist of initiatives around natural language processing platforms, speech analytics, call center AI, and conversational interfaces, with the goal of improving Zillow’s business and customer services using natural language processing and speech understanding.

Before joining Zillow in 2019, Andrei led search science teams within eBay and Walmart. Prior to Walmart he worked at Google and Apple, serving as a core contributor to products like Google web search, Apple Maps, Apple’s AppStore, and iTunes search engines. He previously led engineering efforts for Recruit Holdings’ AI Lab. He founded and sat on the advisory board of Ozlo, a Conversational AI startup acquired by Facebook in 2017.

Andrei earned a Doctor of Philosophy in Computer Science from The University of Manchester, United Kingdom and Master of Science Degree from the Moscow Institute of Physics and Technology.

“Data-Driven Marketing: Framework, Role of Machine Learning and Practice.”
What are the benefits of data driven marketing? Which ML techniques are relevant to specific stages of relating with the customer? How do you convert data to actionable insights? What are the biggest challenges to achieving success? In a hyper-competitive economy, data-driven marketing is crucial to the success of a company. Come learn the strategies and ML techniques necessary to thrive in a customer-centric field.

Dr. Savitha Namuduri, Ph.D. has over 20 years of experience in Data and Analytics space, spanning over several industries such as Telecom, Utility, Retail, Healthcare and CPG. Her analytical skill sets range from developing visualizations, to customer segmentation to building predictive models and democratizing data science techniques to solve day-to-day business problems. Savitha's career has been focused on marketing analytics for the past 10 years running Analytics teams for CRM, media marketing and Customer Journey. In addition to visualization and data analysis, Savitha also focuses on providing data-driven recommendations for strategy, setting up experimental designs, conducting primary and secondary research, and developing learning plans to inform marketing and sales strategies. She brings a unique blend of business acumen and technical skill set to consult with clients on the best marketing practices and their measurement.