The use of machine learning (ML), deep learning (DL) or more generally artificial intelligence (AI) in solving today’s complex business problems has grown exponentially in recent years. Every major business today uses some form of statistical analysis, ML, DL or more generally AI as part of their analytical solutions.
The success of the models in solving business problems, however, varies largely due to complexity of the rules that govern the ever-changing nature of the business data. While one model can yield highly accurate predictions for a period of time, the performance of the same model may degrade over time due to the changing nature of the data.
While most businesses today realize the importance of building robust models, the effort to do so using a multi-pronged approach has still remained limited in many organizations.
Hear our expert discussing his experiences in building AI infrastructure and data science team, and implementing innovative approaches to build robust AI models for a major global financial services client to address some of their challenges.
Some of the important business questions that will be covered through this session are the following
1) How can we build an on-prem infrastructure and in-house data science solutions if needed?
2) How to avoid over-reliance on one algorithm and instead optimize the solutions using a multi-pronged approach?
3) How to continuously evaluate, monitor and improve the performance of the solutions?
and more… Watch now!
