I need help turning a stream of live website-interaction events into actionable intelligence, end-to-end and without lag. The data arrives continuously from our front-end; I want it captured in Apache Kafka, processed on the fly in Python, and then pushed through TensorFlow models for immediate insight—think live segmentation, anomaly flags, or any other quick signal that improves user experience. Here is what I’m after: • A Kafka topic design, producer/consumer code, and the wiring that gets raw clickstream JSON safely into a processing layer. • Stream-oriented preprocessing in Python (windowing, feature extraction, basic validation) that stays fast even at peak traffic. • A TensorFlow pipeline—preferably TF 2.x—that ingests these features and runs inference in real time. I already have GPUs available if you decide they help. • A lightweight output mechanism (another Kafka topic, REST endpoint, or Redis—your call) so my front-end or dashboards can react instantly. Deliverables are the commented source code, a Docker-compose file (or similar) so I can spin the whole stack up locally, and a brief README showing how to feed sample events and see predictions flow through. I’m comfortable iterating in milestones; for example, we can lock down the Kafka layer first, then the TensorFlow graph, then the deployment script. Clean, well-documented work that I can hand off to my ops team is a must.