This white paper helps organizations evaluate and select enterprise data science platforms capable of operationalizing machine learning at scale. It explains why ad hoc tools and siloed workflows prevent data science from delivering business value and outlines the characteristics of a true platform. Key evaluation areas include collaboration across data scientists and developers, security and governance, infrastructure scalability, open-source compatibility, and model deployment lifecycle management. The guide contrasts open-source and proprietary platforms, highlighting flexibility, innovation speed, and talent considerations. It also includes a detailed buyer’s checklist covering integration, analytics tooling, ML capabilities, and operational requirements, enabling organizations to make

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