Autodesk Puts Snowflake at the Core of Its Customer 360
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In a typical machine learning (ML) workflow, data science teams develop features in subsets of data and to take models into production, engineering teams are burdened with re-coding feature pipelines
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The value of machine learning (ML) is largely unrealized, as most models are never deployed in production. This disconnect between experiments and production can be attributed to siloed data, tools, a
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Spark can be a powerful tool in the right situation, but working with Spark isn’t always easy. Migrating Spark to Snowflake aims to enable a wider audience of users to share a common platform and supp
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