Apache Kafka KSQL TensorFlow for Data Scientists via Python Jupyter Notebook

There is an impedance mismatch between model development using Python and its Machine Learning tool stack and a scalable, reliable data platform. The former is what you need for quick and easy prototyping to build analytic models. The latter is what you need to use for data ingestion, preprocessing, model deployment and monitoring at scale. It requires low latency, high throughput, zero data loss and 24/7 availability requirements. This is the main reason I see in the field why companies struggle to bring analytic models into production to add business value.