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3 Critical Criteria for AI Success
A fine-tuned focus on data, an embrace of open standards, and the simplification of processes all accelerate the time to value from AI initiatives.
This content explores:
- Three key stumbling blocks that typically cause many AI initiatives to fail despite the scale and variety of solutions.
- The critical capabilities that are necessary for success
- The ways in which the Databricks Lakehouse Platform, with its data-centric AI approach, helps deliver these capabilities and accelerates time to business value
- The Databricks Lakehouse Platform helps businesses meet these three criteria for AI success. It simplifies and speeds AI while enabling the collaboration required to move from a reactive to a proactive response. Its datacentric approach combines the reliability, performance, and governance of data warehouses with the scalability of data lakes for flexible, high-performance analytics, data science, and machine learning.
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