Nov 27, 2023 · Nov 20, 2023 · Nov 21, 2023 · Nov 24, 2023 · Nov 25, 2023 · Nov 25, 2023
diff --git a/README.md b/README.md
 ```

 ## Tabular data
 - [47+ classification and regression algorithms](https://postgresml.org/docs/guides/training/algorithm_selection)
 - [47+ classification and regression algorithms](https://postgresml.org/docs/training/algorithm_selection)
 - [8 - 40X faster inference than HTTP based model serving](https://postgresml.org/blog/postgresml-is-8x-faster-than-python-http-microservices)
 - [Millions of transactions per second](https://postgresml.org/blog/scaling-postgresml-to-one-million-requests-per-second)
 - [Horizontal scalability](https://github.com/postgresml/pgcat)
    sudo -u postgresml psql -d postgresml
 ```

 For more details, take a look at our [Quick Start with Docker](https://postgresml.org/docs/guides/developer-docs/quick-start-with-docker) documentation.
 For more details, take a look at our [Quick Start with Docker](https://postgresml.org/docs/developer-docs/quick-start-with-docker) documentation.

 # Getting Started

diff --git a/packages/cargo-pgml-components/src/local_dev.rs b/packages/cargo-pgml-components/src/local_dev.rs
 static PG_PGML: &str = "To install PostgresML into your PostgreSQL database,
 follow the instructions on:

 \thttps://postgresml.org/docs/guides/setup/v2/installation
 \thttps://postgresml.org/docs/setup/v2/installation
 ";

 #[cfg(target_os = "linux")]
diff --git a/pgml-dashboard/README.md b/pgml-dashboard/README.md

 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 [quick start instructions](https://postgresml.org/docs/guides/getting-started/sign-up) for general information on installing or deploying PostgresML. A [developer guide](https://postgresml.org/developer_guide/overview/) is also available for those who would like to contribute.
 Please see the [quick start instructions](https://postgresml.org/docs/getting-started/sign-up) for general information on installing or deploying PostgresML. A [developer guide](https://postgresml.org/developer_guide/overview/) is also available for those who would like to contribute.
diff --git a/...content/blog/generating-llm-embeddings-with-open-source-models-in-postgresml.md b/...content/blog/generating-llm-embeddings-with-open-source-models-in-postgresml.md

 ## Generating embeddings from natural language text

 PostgresML provides a simple interface to generate embeddings from text in your database. You can use the [`pgml.embed`](https://postgresml.org/docs/guides/transformers/embeddings) function to generate embeddings for a column of text. The function takes a transformer name and a text value. The transformer will automatically be downloaded and cached on your connection process for reuse. You can see a list of potential good candidate models to generate embeddings on the [Massive Text Embedding Benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
 PostgresML provides a simple interface to generate embeddings from text in your database. You can use the [`pgml.embed`](https://postgresml.org/docs/transformers/embeddings) function to generate embeddings for a column of text. The function takes a transformer name and a text value. The transformer will automatically be downloaded and cached on your connection process for reuse. You can see a list of potential good candidate models to generate embeddings on the [Massive Text Embedding Benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard).

 Since our corpus of documents (movie reviews) are all relatively short and similar in style, we don't need a large model. <code>[intfloat/e5-small](https://huggingface.co/intfloat/e5-small)</code> will be a good first attempt. The great thing about PostgresML is you can always regenerate your embeddings later to experiment with different embedding models.

diff --git a/pgml-dashboard/content/docs/about/faq.md b/pgml-dashboard/content/docs/about/faq.md

 *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](/docs/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](/docs/guides/training/preprocessing/)) like imputing missing values by default and normalizing features 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](/docs/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](/docs/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.

diff --git a/pgml-dashboard/content/docs/guides/dashboard/overview.md b/pgml-dashboard/content/docs/guides/dashboard/overview.md
 # Dashboard

 PostgresML comes with a web app to provide visibility into models and datasets in your database. If you're running [our Docker container](/docs/guides/developer-docs/quick-start-with-docker), you can view it running on [http://localhost:8000/](http://localhost:8000/).
 PostgresML comes with a web app to provide visibility into models and datasets in your database. If you're running [our Docker container](/docs/developer-docs/quick-start-with-docker), you can view it running on [http://localhost:8000/](http://localhost:8000/).


 ## Generate example data
diff --git a/pgml-dashboard/content/docs/guides/predictions/overview.md b/pgml-dashboard/content/docs/guides/predictions/overview.md

 ### Example

 If you've already been through the [Training Overview](/docs/guides/training/overview/), you can see the results of those efforts:
 If you've already been through the [Training Overview](/docs/training/overview/), you can see the results of those efforts:

 === "SQL"


 PostgresML will automatically deploy a model only if it has better metrics than existing ones, so it's safe to experiment with different algorithms and hyperparameters.

 Take a look at [Deploying Models](/docs/guides/predictions/deployments/) documentation for more details.
 Take a look at [Deploying Models](/docs/predictions/deployments/) documentation for more details.

 ## Specific Models

diff --git a/pgml-dashboard/content/docs/guides/schema/deployments.md b/pgml-dashboard/content/docs/guides/schema/deployments.md
 # Deployments

 Deployments are an artifact of calls to `pgml.deploy()` and `pgml.train()`. See [Deployments](/docs/guides/predictions/deployments/) for ways to create new deployments manually.
 Deployments are an artifact of calls to `pgml.deploy()` and `pgml.train()`. See [Deployments](/docs/predictions/deployments/) for ways to create new deployments manually.

 ![Deployment](/dashboard/static/images/dashboard/deployment.png)

diff --git a/pgml-dashboard/content/docs/guides/schema/models.md b/pgml-dashboard/content/docs/guides/schema/models.md
 # Models

 Models are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/guides/training/overview/) for ways to create new models.
 Models are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/training/overview/) for ways to create new models.

 ![Models](/dashboard/static/images/dashboard/model.png)

diff --git a/pgml-dashboard/content/docs/guides/schema/projects.md b/pgml-dashboard/content/docs/guides/schema/projects.md
 # Projects

 Projects are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/guides/training/overview/) for ways to create new projects.
 Projects are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/training/overview/) for ways to create new projects.

 ![Projects](/dashboard/static/images/dashboard/project.png)

diff --git a/pgml-dashboard/content/docs/guides/schema/snapshots.md b/pgml-dashboard/content/docs/guides/schema/snapshots.md
 # Snapshots

 Snapshots are an artifact of calls to `pgml.train()` that specify the `relation_name` and `y_column_name` parameters. See [Training Overview](/docs/guides/training/overview/) for ways to create new snapshots.
 Snapshots are an artifact of calls to `pgml.train()` that specify the `relation_name` and `y_column_name` parameters. See [Training Overview](/docs/training/overview/) for ways to create new snapshots.

 ![Snapshots](/dashboard/static/images/dashboard/snapshot.png)

diff --git a/pgml-dashboard/content/docs/guides/setup/distributed_training.md b/pgml-dashboard/content/docs/guides/setup/distributed_training.md
    -f dump.sql
 ```

 If you're using our <a href="/docs/guides/developer-docs/quick-start-with-docker">Docker</a> stack, you can import the data there:</p>
 If you're using our <a href="/docs/developer-docs/quick-start-with-docker">Docker</a> stack, you can import the data there:</p>

 ```
 psql \
diff --git a/pgml-dashboard/content/docs/guides/setup/gpu_support.md b/pgml-dashboard/content/docs/guides/setup/gpu_support.md
 !!!

 ## Tensorflow
 GPU setup for Tensorflow is covered in the [documentation](https://www.tensorflow.org/install/pip). You may acquire pre-trained GPU enabled models for fine tuning from [Hugging Face](/docs/guides/transformers/fine_tuning/).
 GPU setup for Tensorflow is covered in the [documentation](https://www.tensorflow.org/install/pip). You may acquire pre-trained GPU enabled models for fine tuning from [Hugging Face](/docs/transformers/fine_tuning/).

 ## Torch
 GPU setup for Torch is covered in the [documentation](https://pytorch.org/get-started/locally/). You may acquire pre-trained GPU enabled models for fine tuning from [Hugging Face](/docs/guides/transformers/fine_tuning/).
 GPU setup for Torch is covered in the [documentation](https://pytorch.org/get-started/locally/). You may acquire pre-trained GPU enabled models for fine tuning from [Hugging Face](/docs/transformers/fine_tuning/).

 ## Flax
 GPU setup for Flax is covered in the [documentation](https://github.com/google/jax#pip-installation-gpu-cuda). You may acquire pre-trained GPU enabled models for fine tuning from [Hugging Face](/docs/guides/transformers/fine_tuning/).
 GPU setup for Flax is covered in the [documentation](https://github.com/google/jax#pip-installation-gpu-cuda). You may acquire pre-trained GPU enabled models for fine tuning from [Hugging Face](/docs/transformers/fine_tuning/).

 ## XGBoost
 GPU setup for XGBoost is covered in the [documentation](https://xgboost.readthedocs.io/en/stable/gpu/index.html).
diff --git a/pgml-dashboard/content/docs/guides/setup/installation.md b/pgml-dashboard/content/docs/guides/setup/installation.md

 !!! note

 With the release of PostgresML 2.0, this documentation has been deprecated. New installation instructions are <a href="/docs/guides/setup/v2/installation/">available</a>.
 With the release of PostgresML 2.0, this documentation has been deprecated. New installation instructions are <a href="/docs/setup/v2/installation/">available</a>.

 !!!

diff --git a/pgml-dashboard/content/docs/guides/setup/quick_start_with_docker.md b/pgml-dashboard/content/docs/guides/setup/quick_start_with_docker.md
 4. Save it into the model store (a Postgres table)
 5. Load it and cache it during inference

 Check out our [Training](/docs/guides/training/overview/) and [Predictions](/docs/guides/predictions/overview/) documentation for more details. Some more advanced topics like [hyperparameter search](/docs/guides/training/hyperparameter_search/) and [GPU acceleration](/docs/guides/setup/gpu_support/) are available as well.
 Check out our [Training](/docs/training/overview/) and [Predictions](/docs/predictions/overview/) documentation for more details. Some more advanced topics like [hyperparameter search](/docs/training/hyperparameter_search/) and [GPU acceleration](/docs/setup/gpu_support/) are available as well.

 ## Dashboard

diff --git a/pgml-dashboard/content/docs/guides/setup/v2/installation.md b/pgml-dashboard/content/docs/guides/setup/v2/installation.md

 !!! tip

 If you're just looking to try PostgresML without installing it on your system, take a look at our [Quick Start with Docker](/docs/guides/developer-docs/quick-start-with-docker) guide.
 If you're just looking to try PostgresML without installing it on your system, take a look at our [Quick Start with Docker](/docs/developer-docs/quick-start-with-docker) guide.

 !!!

diff --git a/pgml-dashboard/content/docs/guides/setup/v2/upgrade-from-v1.md b/pgml-dashboard/content/docs/guides/setup/v2/upgrade-from-v1.md

 !!! note

 Make sure you've set up the system requirements in [v2.0 installation](/docs/guides/setup/v2/installation/), so that the v2.0 extension may be installed.
 Make sure you've set up the system requirements in [v2.0 installation](/docs/setup/v2/installation/), so that the v2.0 extension may be installed.

 !!!

diff --git a/pgml-dashboard/content/docs/guides/training/hyperparameter_search.md b/pgml-dashboard/content/docs/guides/training/hyperparameter_search.md

 !!!

 You may pass any of the arguments listed in the algorithms documentation as hyperparameters. See [Algorithms](/docs/guides/training/algorithm_selection/) for the complete list of algorithms and their associated hyperparameters.
 You may pass any of the arguments listed in the algorithms documentation as hyperparameters. See [Algorithms](/docs/training/algorithm_selection/) for the complete list of algorithms and their associated hyperparameters.

 ### Search Algorithms

diff --git a/pgml-dashboard/content/docs/guides/training/overview.md b/pgml-dashboard/content/docs/guides/training/overview.md
 | `task` | The objective of the experiment: `regression` or `classification`. | `classification` |
 | `relation_name` | The Postgres table or view where the training data is stored or defined. | `public.users` |
 | `y_column_name` | The name of the label (aka "target" or "unknown") column in the training table. | `is_bot` |
 | `algorithm` | The algorithm to train on the dataset, see [Algorithm Selection](/docs/guides/training/algorithm_selection/) for details. | `xgboost` |
 | `algorithm` | The algorithm to train on the dataset, see [Algorithm Selection](/docs/training/algorithm_selection/) for details. | `xgboost` |
 | `hyperparams ` | The hyperparameters to pass to the algorithm for training, JSON formatted. | `{ "n_estimators": 25 }` |
 | `search` | If set, PostgresML will perform a hyperparameter search to find the best hyperparameters for the algorithm. See [Hyperparameter Search](/docs/guides/training/hyperparameter_search/) for details. | `grid` |
 | `search` | If set, PostgresML will perform a hyperparameter search to find the best hyperparameters for the algorithm. See [Hyperparameter Search](/docs/training/hyperparameter_search/) for details. | `grid` |
 | `search_params` | Search parameters used in the hyperparameter search, using the scikit-learn notation, JSON formatted. | ```{ "n_estimators": [5, 10, 25, 100] }``` |
 | `search_args` | Configuration parameters for the search, JSON formatted. Currently only `n_iter` is supported for `random` search. | `{ "n_iter": 10 }` |
 | `test_size ` | Fraction of the dataset to use for the test set and algorithm validation. | `0.25` |

 ## Training a Model

 Now that we've got data, we're ready to train a model using an algorithm. We'll start with the default `linear` algorithm to demonstrate the basics. See the [Algorithms](/docs/guides/training/algorithm_selection/) for a complete list of available algorithms.
 Now that we've got data, we're ready to train a model using an algorithm. We'll start with the default `linear` algorithm to demonstrate the basics. See the [Algorithms](/docs/training/algorithm_selection/) for a complete list of available algorithms.


 === "SQL"
 ===


 The output gives us information about the training run, including the `deployed` status. This is great news indicating training has successfully reached a new high score for the project's key metric and our new model was automatically deployed as the one that will be used to make new predictions for the project. See [Deployments](/docs/guides/predictions/deployments/) for a guide to managing the active model.
 The output gives us information about the training run, including the `deployed` status. This is great news indicating training has successfully reached a new high score for the project's key metric and our new model was automatically deployed as the one that will be used to make new predictions for the project. See [Deployments](/docs/predictions/deployments/) for a guide to managing the active model.

 ## Inspecting the results
 Now we can inspect some of the artifacts a training run creates.
diff --git a/pgml-dashboard/content/docs/guides/training/preprocessing.md b/pgml-dashboard/content/docs/guides/training/preprocessing.md
 - [Imputing](#imputing-missing-values) NULL values to some quantitative value
 - [Scaling](#scaling-values) quantitative values across all variables to similar ranges

 These preprocessing steps may be specified on a per-column basis to the [train()](/docs/guides/training/overview/) function. By default, PostgresML does minimal preprocessing on training data, and will raise an error during analysis if NULL values are encountered without a preprocessor. All types other than `TEXT` are treated as quantitative variables and cast to floating point representations before passing them to the underlying algorithm implementations.
 These preprocessing steps may be specified on a per-column basis to the [train()](/docs/training/overview/) function. By default, PostgresML does minimal preprocessing on training data, and will raise an error during analysis if NULL values are encountered without a preprocessor. All types other than `TEXT` are treated as quantitative variables and cast to floating point representations before passing them to the underlying algorithm implementations.

 ```postgresql title="pgml.train()"
 SELECT pgml.train(
diff --git a/pgml-dashboard/content/docs/guides/transformers/pre_trained_models.md b/pgml-dashboard/content/docs/guides/transformers/pre_trained_models.md

 ===

 See [text classification documentation](https://huggingface.co/tasks/text-classification) for more options and potential use cases beyond sentiment analysis. You'll notice the outputs are not great in this example. RoBERTa is a breakthrough model, that demonstrated just how important each particular hyperparameter is for the task and particular dataset regardless of how large your model is. We'll show how to [fine tune](/docs/guides/transformers/fine_tuning/) models on your data in the next step.
 See [text classification documentation](https://huggingface.co/tasks/text-classification) for more options and potential use cases beyond sentiment analysis. You'll notice the outputs are not great in this example. RoBERTa is a breakthrough model, that demonstrated just how important each particular hyperparameter is for the task and particular dataset regardless of how large your model is. We'll show how to [fine tune](/docs/transformers/fine_tuning/) models on your data in the next step.

 ### Summarization
 Sometimes we need all the nuanced detail, but sometimes it's nice to get to the point. Summarization can reduce a very long and complex document to a few sentences. One studied application is reducing legal bills passed by Congress into a plain english summary. Hollywood may also need some intelligence to reduce a full synopsis down to a pithy blurb for movies like Inception.
 ===

 ### More
 There are many different [tasks](https://huggingface.co/tasks) and tens of thousands of state-of-the-art [models](https://huggingface.co/models) available for you to explore. The possibilities are expanding every day. There can be amazing performance improvements in domain specific versions of these general tasks by fine tuning published models on your dataset. See the next section for [fine tuning](/docs/guides/transformers/fine_tuning/) demonstrations.
 There are many different [tasks](https://huggingface.co/tasks) and tens of thousands of state-of-the-art [models](https://huggingface.co/models) available for you to explore. The possibilities are expanding every day. There can be amazing performance improvements in domain specific versions of these general tasks by fine tuning published models on your dataset. See the next section for [fine tuning](/docs/transformers/fine_tuning/) demonstrations.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -108,7 +108,7 @@ SELECT pgml.transform(
		```

		## Tabular data
		- [47+ classification and regression algorithms](https://postgresml.org/docs/guides/training/algorithm_selection)
		- [47+ classification and regression algorithms](https://postgresml.org/docs/training/algorithm_selection)
		- [8 - 40X faster inference than HTTP based model serving](https://postgresml.org/blog/postgresml-is-8x-faster-than-python-http-microservices)
		- [Millions of transactions per second](https://postgresml.org/blog/scaling-postgresml-to-one-million-requests-per-second)
		- [Horizontal scalability](https://github.com/postgresml/pgcat)
Expand DownExpand Up		@@ -154,7 +154,7 @@ docker run \
		sudo -u postgresml psql -d postgresml
		```

		For more details, take a look at our [Quick Start with Docker](https://postgresml.org/docs/guides/developer-docs/quick-start-with-docker) documentation.
		For more details, take a look at our [Quick Start with Docker](https://postgresml.org/docs/developer-docs/quick-start-with-docker) documentation.

		# Getting Started

Expand Down
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Expand Up		@@ -82,7 +82,7 @@ static PG_PGVECTOR: &str = "
		static PG_PGML: &str = "To install PostgresML into your PostgreSQL database,
		follow the instructions on:

		\thttps://postgresml.org/docs/guides/setup/v2/installation
		\thttps://postgresml.org/docs/setup/v2/installation
		";

		#[cfg(target_os = "linux")]
Expand Down
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Expand Up		@@ -2,4 +2,4 @@

		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 [quick start instructions](https://postgresml.org/docs/guides/getting-started/sign-up) for general information on installing or deploying PostgresML. A [developer guide](https://postgresml.org/developer_guide/overview/) is also available for those who would like to contribute.
		Please see the [quick start instructions](https://postgresml.org/docs/getting-started/sign-up) for general information on installing or deploying PostgresML. A [developer guide](https://postgresml.org/developer_guide/overview/) is also available for those who would like to contribute.
Original file line number	Diff line number	Diff line change
Expand Up		@@ -121,7 +121,7 @@ LIMIT 5;

		## Generating embeddings from natural language text

		PostgresML provides a simple interface to generate embeddings from text in your database. You can use the [`pgml.embed`](https://postgresml.org/docs/guides/transformers/embeddings) function to generate embeddings for a column of text. The function takes a transformer name and a text value. The transformer will automatically be downloaded and cached on your connection process for reuse. You can see a list of potential good candidate models to generate embeddings on the [Massive Text Embedding Benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
		PostgresML provides a simple interface to generate embeddings from text in your database. You can use the [`pgml.embed`](https://postgresml.org/docs/transformers/embeddings) function to generate embeddings for a column of text. The function takes a transformer name and a text value. The transformer will automatically be downloaded and cached on your connection process for reuse. You can see a list of potential good candidate models to generate embeddings on the [Massive Text Embedding Benchmark leaderboard](https://huggingface.co/spaces/mteb/leaderboard).

		Since our corpus of documents (movie reviews) are all relatively short and similar in style, we don't need a large model. <code>[intfloat/e5-small](https://huggingface.co/intfloat/e5-small)</code> will be a good first attempt. The great thing about PostgresML is you can always regenerate your embeddings later to experiment with different embedding models.

Expand Down
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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](/docs/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](/docs/guides/training/preprocessing/)) like imputing missing values by default and normalizing features 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](/docs/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](/docs/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.

Expand Down
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		@@ -1,6 +1,6 @@
		# Dashboard

		PostgresML comes with a web app to provide visibility into models and datasets in your database. If you're running [our Docker container](/docs/guides/developer-docs/quick-start-with-docker), you can view it running on [http://localhost:8000/](http://localhost:8000/).
		PostgresML comes with a web app to provide visibility into models and datasets in your database. If you're running [our Docker container](/docs/developer-docs/quick-start-with-docker), you can view it running on [http://localhost:8000/](http://localhost:8000/).


		## Generate example data
Expand Down
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Expand Up		@@ -51,7 +51,7 @@ LIMIT 25;

		### Example

		If you've already been through the [Training Overview](/docs/guides/training/overview/), you can see the results of those efforts:
		If you've already been through the [Training Overview](/docs/training/overview/), you can see the results of those efforts:

		=== "SQL"

Expand DownExpand Up		@@ -106,7 +106,7 @@ SELECT * FROM pgml.deployed_models;

		PostgresML will automatically deploy a model only if it has better metrics than existing ones, so it's safe to experiment with different algorithms and hyperparameters.

		Take a look at [Deploying Models](/docs/guides/predictions/deployments/) documentation for more details.
		Take a look at [Deploying Models](/docs/predictions/deployments/) documentation for more details.

		## Specific Models

Expand Down
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		@@ -1,6 +1,6 @@
		# Deployments

		Deployments are an artifact of calls to `pgml.deploy()` and `pgml.train()`. See [Deployments](/docs/guides/predictions/deployments/) for ways to create new deployments manually.
		Deployments are an artifact of calls to `pgml.deploy()` and `pgml.train()`. See [Deployments](/docs/predictions/deployments/) for ways to create new deployments manually.

		![Deployment](/dashboard/static/images/dashboard/deployment.png)

Expand Down
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		@@ -1,6 +1,6 @@
		# Models

		Models are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/guides/training/overview/) for ways to create new models.
		Models are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/training/overview/) for ways to create new models.

		![Models](/dashboard/static/images/dashboard/model.png)

Expand Down
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		@@ -1,6 +1,6 @@
		# Projects

		Projects are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/guides/training/overview/) for ways to create new projects.
		Projects are an artifact of calls to `pgml.train()`. See [Training Overview](/docs/training/overview/) for ways to create new projects.

		![Projects](/dashboard/static/images/dashboard/project.png)

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