-[47+ classification and regression algorithms](https://postgresml.org/docs/api/sql-extension/pgml.train/)

-[47+ classification and regression algorithms](/docs/open-source/pgml/api/pgml.train)

-[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)

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

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

During training, model is periodically uploaded to Hugging Face Hub. You will find the model at`https://huggingface.co/<username>/<project_name>`. An example model that was automatically pushed to Hugging Face Hub is[here](https://huggingface.co/santiadavani/imdb_review_sentiement).

Now, that we have fine-tuned model on Hugging Face Hub, we can use[`pgml.transform`](https://postgresml.org/docs/introduction/apis/sql-extensions/pgml.transform/text-classification) to perform real-time predictions as well as batch predictions.

Now, that we have fine-tuned model on Hugging Face Hub, we can use[`pgml.transform`](/docs/open-source/pgml/guides/llms/text-classification) to perform real-time predictions as well as batch predictions.

**Real-time predictions**

!!!

We used the[pgml.embed](/docs/api/sql-extension/pgml.embed) PostresML function to generate an embedding of the sentence "Generating embeddings in Postgres is fun!" using the[mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) model from mixedbread.ai.

We used the[pgml.embed](/docs/open-source/pgml/api/pgml.embed) PostresML function to generate an embedding of the sentence "Generating embeddings in Postgres is fun!" using the[mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) model from mixedbread.ai.

The output size of the vector varies per model, and in`mxbai-embed-large-v1` outputs vectors with 1024 dimensions: each vector contains 1024 floating point numbers.

This app is far from complete but does show an easy and scalable way to get started with ML in Express. From here I encourage you to head over to our[docs](https://postgresml.org/docs/api/sql-extension/) and see what other features could be added.

This app is far from complete but does show an easy and scalable way to get started with ML in Express. From here I encourage you to head over to our[docs](https://postgresml.org/docs/) and see what other features could be added.

If SQL is not your thing, no worries. Check out or[JS SDK](https://postgresml.org/docs/api/client-sdk/getting-started) to streamline all our best practices with simple JavaScript.&#x20;

If SQL is not your thing, no worries. Check out or[JS SDK](https://postgresml.org/docs/open-source/korvus) to streamline all our best practices with simple JavaScript.&#x20;

We love hearing from you — please reach out to us on[Discord](https://discord.gg/DmyJP3qJ7U)or simply[Contact Us](https://postgresml.org/contact) here if you have any questions or feedback.&#x20;

Advanced search engines like Google use this technique to extract the meaning of search queries and rank the results based on what the user actually_wants_, unlike simple keyword matches which can easily give irrelevant results.

To accomplish this, for each document in our app, we include an embedding column stored as a vector. A vector is just an array of floating point numbers. For each item in our to-do list, we automatically generate the embedding using the PostgresML[`pgml.embed()`](https://postgresml.org/docs/introduction/apis/sql-extensions/pgml.embed) function. This function runs inside the database and doesn't require the Django app to install the model locally.

To accomplish this, for each document in our app, we include an embedding column stored as a vector. A vector is just an array of floating point numbers. For each item in our to-do list, we automatically generate the embedding using the PostgresML[`pgml.embed()`](/docs/open-source/pgml/api/pgml.embed) function. This function runs inside the database and doesn't require the Django app to install the model locally.

An embedding model running inside PostgresML is able to extract the meaning of search queries & compare it to the meaning of the documents it stores, just like a human being would if they were able to search millions of documents in just a few milliseconds.

These capabilities are primarily provided by two open-source software projects, that may be used independently, but are designed to be used together with the rest of the Postgres ecosystem:

*[**pgml**](/docs/api/sql-extension/) - an open source extension for PostgreSQL. It adds support for GPUs and the latest ML & AI algorithms_inside_ the database with a SQL API and no additional infrastructure, networking latency, or reliability costs.

*[**PgCat**](/docs/product/pgcat/) - an open source connection pooler for PostgreSQL. It abstracts the scalability and reliability concerns of managing a distributed cluster of Postgres databases. Client applications connect only to the pooler, which handles load balancing, sharding, and failover, outside of any single database server.

*[**pgml**](/docs/open-source/pgml/) - an open source extension for PostgreSQL. It adds support for GPUs and the latest ML & AI algorithms_inside_ the database with a SQL API and no additional infrastructure, networking latency, or reliability costs.

*[**PgCat**](/docs/open-source/pgcat/) - an open source connection pooler for PostgreSQL. It abstracts the scalability and reliability concerns of managing a distributed cluster of Postgres databases. Client applications connect only to the pooler, which handles load balancing, sharding, and failover, outside of any single database server.

<figure><imgsrc=".gitbook/assets/architecture.png"alt="PostgresML architectural diagram"><figcaption></figcaption></figure>

To learn more about how we designed PostgresML, take a look at our[architecture overview](/docs/resources/architecture/).

The PostgresML team also provides[native language SDKs](/docs/api/client-sdk/) which implement best practices for common ML & AI applications. The JavaScript and Python SDKs are generated from the a core Rust library, which provides a uniform API, correctness and efficiency across all environments.

The PostgresML team also provides[native language SDKs](/docs/open-source/korvus/) which implement best practices for common ML & AI applications. The JavaScript and Python SDKs are generated from the a core Rust library, which provides a uniform API, correctness and efficiency across all environments.

While using the SDK is completely optional, SDK clients can perform advanced machine learning tasks in a single SQL request, without having to transfer additional data, models, hardware or dependencies to the client application.