What kind of reskilling/hiring initiatives businesses need to become AI driven?
Data Consultant, Litix Gmbh
Dr. Nitin Kumar has over a decade experience in creating machine learning based business applications in his roles as a data scientist, educator and consultant. He has delivering around 100 Data Science proof-of-concepts and a dozen end-to-end data products. Last couple of years his focus has been on designing and delivering ML trainings on topics including machine learning for managers, use-case identification and MLOps to push the pace of ML productionisation across several industries e.g. insurance, transportation, manufacturing.... Teaching is his passion which he does as part of his consulting role.
There is no question that reskilling/hiring is on every organisation's agenda in their goal to become data-driven. But how do you determine who should be reskilled/hired and when (HR, IT, Data Scientists, Engineers, Senior Management)? What are the common barriers to reskilling/hiring (internal vs external barriers)? Who should bear the cost of reskilling? How does one quantify the effect of reskilling? In the keynote, we will explore some of the questions mentioned here in more depth and try to develop an awareness around what it takes to build a successful reskilling/hiring strategy.