Moneta has repeatedly been recognized as the most innovative bank on the Czech market. This is due in large part to their strategy of completely shifting to the cloud and using data and advanced analytics to innovate the customer experience with use cases ranging from real-time recommendations to fraud detection.
In this talk, we’ll share how we migrated to the cloud to create an agile environment for analytics and AI. From rapid prototyping machine learning use cases to moving models into production, core to this approach was building a unified platform for data and analytics on Apache Spark, Databricks and AWS. Discussion topics include:
DataSentics a.s.
Milan Berka is a ML architect at DataSentics a.s. After he finished his mathematics and stochastics college degree, he started pursuing a career of a data scientist. However, soon it became clear that without a proper data infrastructure and data engineering element, it is very difficult to make a lasting impact with any data science model - regardless of how great the model itself is. Therefore, almost four years ago, he jumped over to "more engineering side" and started building experience in cloud infrastructure, big data frameworks, DevOps practices and other engineering topics. Combining the machine learning and engineering knowledge, his primary focus now is designing and building solutions which ease or even enable the productionalization of machine learning models (MLOps).
