How DuckDB’s in-database machine studying modifications the way in which we take into consideration knowledge workflows.
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Learn to prepare machine studying fashions immediately inside DuckDB with out exporting knowledge — quicker, less complicated, and scalable.
If you happen to’re like most knowledge scientists, you most likely export knowledge out of your database into Pandas, scikit-learn, or PyTorch earlier than coaching a mannequin.
However what when you didn’t want to maneuver your knowledge in any respect?
DuckDB — typically known as the “SQLite for analytics” — is bringing machine studying nearer to the info with in-database coaching. This implies fewer exports, quicker iterations, and less complicated pipelines.
Let’s discover how this works and why it issues.
Conventional workflow:
Question knowledge with SQL.
Export to Pandas/NumPy.
Practice a mannequin with scikit-learn.
This back-and-forth has prices:
Efficiency hit: Copying gigabytes of information is gradual.
Complexity: You juggle SQL + Python code.
Reminiscence points: Pandas struggles with very massive datasets.
