The focus of this panel is manifold, the idea here is with a new advent of the revolution being brought about in our life, career, and jobs with AI, how do we ensure success in our ever-evolving careers and also ensure success for the enterprises, companies we work in and the businesses we run. There will be three major themes for this panel:
- Enabling Successful Adoption of AI in your organization: What does it really take to move from a data-driven company to be an AI-driven company? How do we tackle problems AI\Data first? What kind of workflows and processes are followed to take an AI product or solution from conception to production? How much importance should be given to understanding business problems, do we notice any gaps between the business and data scientists\engineers. How important is engineering to ensure the success of an AI product? How do we monitor the performance of AI models after production? How do we make our clients, business, C-level execs embrace change in existing processes with AI
- Job Roles in Data Science\AI and what they do: The ever-changing job landscape has been hit with a multitude of roles pertaining to data science and AI. We have data scientists, data engineers, ML engineers, DL engineers, data analysts, business analysts, ML architect and many more. What are some of the major job roles or rather tasks which people have to do in this field? What is a typical day in the life of a data scientist\manager? How do we measure and ensure success in these new job titles? What are some of the key use-cases panelists might have solved to drive their own careers towards success? Are creativity and innovation often dying out in larger companies which suffocate data scientists? etc
- How to build a successful career in data science: I’m a student, there is literally an overload of information out there with regard to data science resources, bootcamps, trainings, lectures, courses, degrees. Almost everyone seems to be “doing data science”. What do I need to learn, practice, implement and work towards so I can break into this field. What are the key pitfalls and challenges I should be careful about? How do I make sure I don’t get too deep and disillusioned into the AI hype?
Moderator: Maheshwari Dhandapani
