What is Real-Time Machine Learning?

Real-time machine learning involves training machine learning models using live, streaming data. Unlike traditional machine learning, which relies on historical, static datasets, this approach allows models to adapt and learn continuously.

Real-time machine learning excels in scenarios where data is scarce or subject to rapid shifts. By continuously learning from new data streams, models can adapt to emerging patterns and trends. This is evident in applications like product recommendation engines, where preferences evolve over time. Instead of relying on periodic retraining, real-time machine learning enables models to adjust dynamically, leading to more accurate and personalized recommendations.

Machine Learning Deployment

Real-time machine learning models are often deployed using an event-driven architecture. This involves setting up a data pipeline that continuously ingests and processes incoming data. The pipeline transforms and enriches the raw data to make it suitable for model input. In parallel, the model itself is updated, along with its reference data, using the live data stream. This iterative process allows the model to adapt and improve over time.

A key element of real-time machine learning architectures is the feature store. This data store houses reference data that is continually updated with fresh data points from the stream. The feature store serves as the training dataset for the model. To accommodate high-velocity data streams, feature stores often leverage in-memory technologies, which provide low-latency access to the data.