Accelerating Machine Learning Workflows for Financial Services

Artificial intelligence has become a key ingredient in the successful digital transformation of financial institutions. Whether it’s about predicting risk, fighting financial crime or improving customer interaction, it is well established that deriving insights from data through machine learning requires having access to quality data, and the ability to run resource-intensive model training workflows at scale. But getting the most from your investment in machine learning requires far more than that: AI leaders need to focus on the entire machine learning workflow and enable frictionless interaction between the different groups that collaborate as part of the process: data scientists, developers, operators. They must also monitor, observe, and continuously adapt their models to changes in external conditions or to the insights derived from the data. Machine learning initiatives in financial institutions add additional challenges such as high reliability, regulatory compliance, and security. In this first webinar of the series, we will show how OpenShift as a platform provides key answers to the challenges of implementing machine learning workflows for financial institutions, how it can help them accelerate AI adoption and help establish them as leaders. Presented by: William Benton, Senior Principal Software Engineer, Data Science and AI, Red Hat Marius Bogoevici, Chief Solutions Architect, Financial Services, Red Hat