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Link to docs from FAQ #521

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Expand Up @@ -10,7 +10,7 @@ Postgres is widely considered mission critical, and some of the most [reliable](

*How good are the models?*

Model quality is often a trade-off between compute resources and incremental quality improvements. Sometimes a few thousands training examples and an off the shelf algorithm can deliver significant business value after a few seconds of training. PostgresML allows stakeholders to choose several different algorithms to get the most bang for the buck, or invest in more computationally intensive techniques as necessary. In addition, PostgresML automatically applies best practices for data cleaning like imputing missing values by default and normalizing data to prevent common problems in production.
Model quality is often a trade-off between compute resources and incremental quality improvements. Sometimes a few thousands training examples and an off the shelf algorithm can deliver significant business value after a few seconds of training. PostgresML allows stakeholders to choose several [different algorithms](/user_guides/training/algorithm_selection/) to get the most bang for the buck, or invest in more computationally intensive techniques as necessary. In addition, PostgresML can automatically apply best practices for [data cleaning](/user_guides/training/preprocessing/)) like imputing missing values by default and normalizing features to prevent common problems in production.

PostgresML doesn't help with reformulating a business problem into a machine learning problem. Like most things in life, the ultimate in quality will be a concerted effort of experts working over time. PostgresML is intended to establish successful patterns for those experts to collaborate around while leveraging the expertise of open source and research communities.

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