In today’s fast-paced world, where every millisecond counts, the ability to predict outcomes quickly and accurately is crucial. This is particularly true in fields such as finance, healthcare, and e-commerce, where timely decisions can mean the difference between success and failure. In this talk, we will explore the challenges associated with achieving millisecond prediction latencies, and discuss strategies for cost-effective training of predictive models. We will also examine techniques for ensuring the sanity of offline versus online performance of prediction models, as well as ways to enable faster experimentation through multiple versions of prediction models. Additionally, we will discuss the problem of model degradation due to data drifts or assumption changes, and explore solutions for addressing this issue. Finally, we will delve into the complexities of solving for batch and real-time use cases of prediction models, and discuss best practices for achieving optimal performance in both scenarios. Join us for an informative and thought-provoking discussion on the latest developments in predictive modelling, and learn how to stay ahead of the curve in this rapidly evolving field.