In 2022, financial services firms faced over $8 billion of fines for anti-money laundering (AML) process failures. And for many, things aren’t getting better—false positives keep rising right along with client expectations. Regulations continually increase in number and complexity and criminals are getting ever more sophisticated in their tactics and techniques.
Organizations today are looking to do more with less. The solution for many? Digital transformation. While digital transformation isn’t a new concept, the benefits of boosting efficiency, controlling costs, and delivering better customer experiences are obvious in today’s topsy-turvy economic conditions. Digital transformation often involves making the transition from legacy monoliths to modern cloud native microservices-based architectures.
The data landscape is constantly evolving, and with it come new challenges and opportunities for data teams. While generative AI and large language models (LLMs) seem to be all everyone is talking about, they are just the latest manifestation of a trend that has been evolving over the past several years: organizations tapping into petabyte-scale data volumes and running increasingly massive data pipelines to deliver ever more data analytics projects and AI/ML models.
With production traffic and automatic mocks, this guide shows how to regression test performance in your Kubernetes cluster.
