At its core, an operational ML platform brings together many teams from different departments; Data Engineers, Data Scientists, ML engineers and Domain Experts can work together to create hundreds of ML pipelines and bring their full expertise to add value to their organization within the same ecosystem.
Team collaboration with the feature store:
Breaking Silos
Break down data silos by providing a centralized location for storing and managing ML features. With support for online and offline feature stores, Hopsworks enables teams to collaborate on feature engineering and ensure consistency across ML pipelines.
Governance and Ownership
Robust governance and ownership capabilities, ensuring that data is accessible only to those who have the appropriate permissions. With support for role-based project access control and metadata labeling and tags.
Data Contracts
Powerful way to manage data dependencies across ML pipelines. By defining contracts that specify the expected schemas and semantics of data, teams can ensure consistency and avoid errors when passing data between different stages of the pipeline
Silos no more!
Depending on the AI maturity of the organization, historic data and/or real time data can be utilized in the ML model. Historic features are typically stored in different data lakes while real-time data is stored in a variety of other services. This results in inconsistency between different stages in the ML-pipeline, from feature engineering, model training and deployment. For example, data scientists will find it hard to move features from feature engineering to deployment whilst data engineers will face challenges guaranteeing data consistency.
Another problem is that features can be stored differently depending on the source. Different teams within your organization add another level of complexity if data is stored in various locations.
It can become almost impossible to know which features exist and whether they are up-to-date and accurate. This prevents teams within an organization from collaborating in the ML pipeline and reusing features.
With Hopsworks; move beyond silo-ing and use a centralized location for all ML data and enable your organization to leverage the full capabilities of your data and teams.
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