Link to docs from FAQ (#521)

montanalow · montanalow · commit 75bccda61a9a · 2023-01-30T17:45:38.000-08:00
Co-authored-by: Montana Low &lt;montana.low@gmail.com&gt;
diff --git a/pgml-dashboard/README.md b/pgml-dashboard/README.md
@@ -0,0 +1,36 @@
+# PostgresML Dashboard
+
+PostgresML provides a dashboard with analytical views of the training data and model performance, as well as integrated notebooks for rapid iteration. It is primarily written in Rust using [Rocket](https://rocket.rs/) as a lightweight web framework and [SQLx](https://github.com/launchbadge/sqlx) to interact with the database.
+
+Please see the [online documentation](https://postgresml.org/user_guides/setup/quick_start_with_docker/) for general information on installing or deploying PostgresML. This document is intended to help developers set up a local copy of the dashboard. 
+
+## Requirements
+
+The dashboard requires a Postgres database with the [pgml-extension](https://github.com/postgresml/postgresml/tree/master/pgml-extension) to generate the core schema. See that subproject for developer setup.
+
+We develop and test this web application on Linux, OS X, and Windows using WSL2.
+
+## Build process
+
+You'll need to specify a database url for the extension to interact with via an environment variable:
+
+```commandline
+export DATABASE_URL=postgres://user_name:password@localhost:5432/database_name
+```
+
+Build and run:
+
+```commandline
+cargo run
+```
+
+Incremental and automatic compilation for development cycles is supported with: 
+
+```commandline
+cargo watch --exec run
+```
+
+Run tests:
+```commandline
+cargo test
+```
diff --git a/pgml-docs/docs/about/faq.md b/pgml-docs/docs/about/faq.md
@@ -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.
 
