Challenges of Deploying AI Models
It is no longer news that the artificial intelligence summer we’ve been experiencing for quite a while now is here to stay. AI promises considerable benefits for businesses and economies through its contributions to productivity and efficiency. At the same time, the potential challenges to adoption cannot be ignored. In this whitepaper, we focus on these from a model risk perspective. Here we explore the so-called deployment challenge, highlighting the difficulties in terms of HR, technical know-how and infrastructure required to productionize AI applications.
Download now to discover:
- The main principles related to productionizing AI
- The maximization of a Model Risk Management Framework in dealing with the complexities of machine learning algorithms; highlighting the key differences compared to the management of more traditional models
“The cornerstone of any model risk management framework is the validation procedure since this guarantees that models are only deployed when certain quality standards are met.” – Jos Gheerardyn
Jos Gheerardyn has built the first FinTech platform that uses AI for real-time model testing and validation on an enterprise-wide scale. A zealous proponent of model risk governance & strategy, Jos is on a mission to empower quants, risk managers and model validators with smarter tools to turn model risk into a business driver. Prior to his current role, he has been active in quantitative finance both as a manager and as an analyst.
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