Building a Real-Time ML Pipeline with a Feature Store - MLOps Live #16

With the growing business demand for real-time use cases such as NLP, fraud prediction, predictive maintenance and real-time recommendations, ML teams are feeling immense pressure to solve the operational challenges of real-time feature engineering for machine learning, in a simple and reproducible way. This is where online feature stores come in. An online feature store accelerates the development and deployment of online AI applications by automating feature engineering and providing a single pane of glass to build, share and manage features across the organization. This improves model accuracy, even when complex calculations and data transformation is involved, saving your team valuable time and providing seamless integration with training, serving and monitoring frameworks.

[ronifintech]

Q: if the data from the Kafka streams are needed to process an inference request, how do you guarantee to have the data in the feature store before processing the inference request?