AI in practice: Integrating algorithms in existing IT-infrastructure and business processes
Artificial Intelligence is finding its way into more and more use cases in private and business life. However, introducing AI or machine learning in business is challenging. Many projects fail after the proof of concept and, although the algorithms are successful, they are not integrated in daily business. Often the reason for this is simple: When developing AI algorithms, the focus is on the technical side, i.e. data preparation, training, testing, evaluation, etc. For getting AI in production, new challenges arrive by integration in existing IT-infrastructure or adjusting the business processes. While many frameworks exist to support the technical lifecycle of AI algorithms (e.g. MLOps tools) there is only limited tool support for the IT and process integration of AI solutions. In this lecture we will discuss challenges to bring AI algorithms in production, present options to handle IT- and process integration and show successful real-life examples.