Building a Real-Time ML Pipeline with a Feature Store

One of the most difficult challenges in operationalizing machine learning is feature engineering with live or production data. Generating features from real-time or online production data is far more complex than with historical data, and requires dedicated implementation by engineering teams. Real-time pipelines require an extremely fast and low-latency event-processing mechanism, that can run complex algorithms to calculate features in real time.

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.

Watch this session to hear about:

• The challenges associated with online feature engineering across training and serving environments
• How feature stores enable teams to collaborate on building, sharing and managing online and offline features across the organization
• Solutions that exist to enable you to build a real-time operational ML pipeline that can handle events arriving in ultra-high velocity and high volume, calculate and trigger an action immediately
• How to build your ML pipeline in a way that enables ingestion and analysis of real-time data and enrichment with historical data to generate meaningful features for inferencing and model re-training.
• How to monitor your real-time AI applications in production to detect and mitigate anomalies or drift, to make your method repeatable and resilient to changes in market conditions.

This blog has been republished by AIIA. To view the original article, please click HERE.