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.

Watch this video from the Machine Learning Summit to learn how to use MLOps to build a real-time ML pipeline for fraud prediction with a special focus in the finance space.

Watch this video to learn more about the MLOps challenge, enabling tools and technologies, and Ecolab's journey with MLOps and automation.

The Iguazio Data Science Platform enables you to develop, deploy and manage real-time AI applications at scale. It provides data science, data engineering and DevOps teams with one platform to operationalize machine learning and rapidly deploy operational ML pipelines. The platform includes an online and offline feature store, fully integrated with automated model monitoring and drift detection, model serving and dynamic scaling capabilities, all packaged in an open and managed platform.

Accelerating Data Science to Production with MLOps Best Practices

In this panel, experts from NetApp, NVIDIA, Strategy Analytics and Iguazio will discuss the main use cases, and also examine the challenges and solutions that exist for building AI applications for connected vehicles.