Automotive Industry

Leverage the power of your data with Hopsworks Feature Store for Machine Learning

Data is revolutionizing the automotive industry and a key area of competitive advantage is the platforms used for data. Hopsworks is an open-source horizontally scalable platform that supports the ingestion of data from many sources at scale, storage of massive volumes of data at low cost, processing that data both in real-time (streaming analytics) and offline (batch analytics), and using machine learning and deep learning to build predictive models in-place on the data. Hopsworks includes au nique multi-tenant security model and support for the most advanced machine and deep learning processing with GPUs.

Hopsworks comes with a user-friendly web interface for running, managing, and accessing data and programs and supports secure 2-factor authentication as well as integration with authentication systems, such as LDAP, ActiveDirectory, OpenAuth-2. Internally, all communications between services is encrypted with TLS/SSL and data can be encrypted at rest, providing Enterprise-grade security for your data. Hopsworks is designed to enable UI-driven, secure collaboration between Data Owners, who are responsible (according to GDPR) for managing access to sensitive data, and researchers who wish to process that data. By default, researchers are prevented from importing data into or exporting data from sandboxed projects - projects are a mechanism to help ensure thatsensitive medical data can be stored in a shared cluster, but only made accessible to those individuals who needthe data when they need it

In the Feature Store, Data Engineers typically have main responsibility for adding new features to the feature store. New features areadded to meet new requirements from Data Scientists. However, if the feature is a simple SQL string for an external datastore, thenData Scientists can often handle such features themselves. Hopsworks supports the concept of projects. A project is a secure repositoryof data and code and members, where ach member has either a data owner role or a more restricted data scientist role. Each projectcan have its own FeatureStore. This way organizations can have a global feature store for less sensitive features (in a global project thatall employees are a member of), while sensitive features can reside in a closed project with control over which users have access to thefeatures. Features can be defined either in applications (Python/Scala/Java) or in the Hopsworks UI (for example, for simple featuresthat are SQL queries on external databases).

 Hopsworks for Automotive