Key requirements for an enterprise ML platform
To deliver business benefits through ML at scale, organizations must have the capability to rapidly experiment with diverse scientific approaches, ML technologies, and extensive datasets. Once ML models are trained and validated, they need to seamlessly transition to production deployment. While some similarities exist between a traditional enterprise software system and an ML platform, such as scalability and security concerns, an enterprise ML platform presents distinctive challenges. These include the need to integrate with the data platform and high-performance computing infrastructure to facilitate large-scale model training.
Let’s delve into some specific core requirements of an enterprise ML platform to meet the needs of different users and operators:
- Support for the end-to-end ML lifecycle: An enterprise ML platform must cater to both data science experimentation and production-grade operations and deployments...
