Hall 2: Knowledge Talks

‘AI’ used in mission-critical uses cases like autonomous cars, financial institutions, healthcare etc., carries a
substantial financial and societal risk. It is required to address the specifications/requirements of all stakeholders
like – Customers, Business owners, Product owners, the Data Science team, Risk managers and Regulators.
Requirements like – how did the model work for a prediction? Was there a bias? Did the team follow all necessary
steps before pushing the model into production? Can I provide satisfactory evidence about model functioning to
regulators? How can I audit my AI models? Was the model consistent in production? When should I retrain my
model? What was the reason for the error in production? etc.

A good ML Observability framework can solve this problem at scale for businesses. In this session, you’ll learn
more about the components of good ML Observability tools for mission-critical AI solutions and successful case

Sustainable Development Goals (SDG have been setup by the United Nations in 2015 and intended to be achieved by 2030. In total, 17 SDGs have been identified some notable ones being No Poverty, Zero Hunger, Quality Education, Good health, Clean Water, Sustainable Energy, Affordable and Clean energy. The current pace of incremental development in those focus areas makes it almost impossible to attain the goals by 2030.

This talk focusses on how some of the important SDGs could be achieved by the use of Artificial Intelligence.

While organizations have been using internal data to sharpen their analytics initiatives, companies now have exposure to numerous external data sources such as location, customer preferences, shopping trends, market news, app usage and social media sentiments that if strategically integrated can enable sharp business decisions. According to a Deloitte survey, over 90% data analytics professionals realize the importance of utilizing external data sources to get better insights on shifting consumer behavior, market insights and competitor initiatives. This talk covers how companies can internalize the external or third-party data to unlock improvements in their analytics initiatives.

Airlines have always been at the cutting edge of leveraging data and data science in business operations.
This talk will provide an exposure to the spectrum of data science problems that are addressed and solved on a daily basis to provide you the safe, reliable and comfortable flying experience that you have come to expect from your favorite airline.

Artificial Intelligence (AI) is transforming every aspect of our lives. It is reshaping our economy, society and our lifestyle. Human interface and relationship with machine is undergoing significant changes. Promises of AI are plenty for mankind as well as for enterprises. However with such a power technology, there is a need for responsible usage of AI.

Catch Hasit in his session to learn his thoughts on this topic.

Adopting a data driven decision making culture involves many aspects – from upgrading the data management, analytics, governance to and probably most importantly, upgrading the decision making paradigm.

The speaker talks about experiences in transforming organization from traditional analytics eco-systems, to the modern, machine learning, fast paced and nimble organization winning in the market.

While opportunities to learn and grow has always featured as a key driver of great work culture and employee engagement, business and L&D has traditionally not been confident of formally stating dependence of business sustenance and growth on employee upskilling.

Join this panel discussion to hear eminent business leaders engage in a fact-based discussion to establish or debunk this linkage.

In this talk she discusses the current state of applications, the way to progress and five principles of cloud-native architecture that will help customers design systems to take full advantage of the cloud while avoiding the pitfalls of shoe-horning old approaches into a new platform.

Businesses have started to realize the potential of AI/ML solutions for transformational gains. While the potential upsides to AI/ML engagement are evident, these solutions also bring with them new challenges and threats, making organizations susceptible to risks. These challenges can arise due to different reasons such as Data Management, Cybersecurity, Algorithmic Bias, or Behaviour Manipulation. A well-defined Responsible AI strategy with a robust framework for implementation enables leaders to overcome such risks and safeguard ethical, legal, environmental, and social concerns.

Our healthcare system is broken: it’s expensive, slow-moving, and even unattainable to some. So, where do we go from here? As the healthcare system looks to adopt newer technologies, is AI the answer to mending a broken system? In this session, hear on the future of AI in health and its role in improving access and affordability.