Navigating Event-Based Data in ML

This chapter explores a paradigm shift towards event-based data handling in machine learning, emphasizing the prevention of information leakage and the importance of time-sensitive data management. Key discussions include the implementation of efficient data frameworks, the use of Parquet file formats, and the integration of Kubernetes for real-time model inference. It also provides insights into sharding, resource allocation, and practical strategies for organizations starting their machine learning journeys.