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

- [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.

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.

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.

In this application, we'll be applying sentiment analysis to note taking. Note taking and journaling can be an excellent practice for work efficiency and self improvement. However, if you are like me, it quickly becomes impossible to find and make use of anything I've written down. Notes that are useful must be easy to navigate. With this motivation, let's create a demo that can record notes throughout the day. Each day will have a summary and sentiment score. That way, if I'm looking for that time a few weeks ago when we were frustrated with our old MLOps platform — it will be easy to find. 

We are going to use [postgresql-client](https://www.npmjs.com/package/postgresql-client) to connect to our DB. 

* `app.get(“/", async (req, res, next)` which returns all the notes for that day and the daily summary. 

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.

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. 

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. 

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.

* [**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.

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.

* [pgml.transform()](open-source/pgml/api/pgml.transform/README.md)

* [Fill-Mask](open-source/pgml/api/pgml.transform/fill-mask.md)

* [Question answering](open-source/pgml/api/pgml.transform/question-answering.md)

* [Summarization](open-source/pgml/api/pgml.transform/summarization.md)

* [Text classification](open-source/pgml/api/pgml.transform/text-classification.md)

* [Text Generation](open-source/pgml/api/pgml.transform/text-generation.md)

* [Text-to-Text Generation](open-source/pgml/api/pgml.transform/text-to-text-generation.md)

* [Token Classification](open-source/pgml/api/pgml.transform/token-classification.md)

* [Translation](open-source/pgml/api/pgml.transform/translation.md)

* [Zero-shot Classification](open-source/pgml/api/pgml.transform/zero-shot-classification.md)

* [pgml.train()](open-source/pgml/api/pgml.train/README.md)

* [Regression](open-source/pgml/api/pgml.train/regression.md)

* [Classification](open-source/pgml/api/pgml.train/classification.md)

* [Clustering](open-source/pgml/api/pgml.train/clustering.md)

* [Decomposition](open-source/pgml/api/pgml.train/decomposition.md)

* [Data Pre-processing](open-source/pgml/api/pgml.train/data-pre-processing.md)

* [Hyperparameter Search](open-source/pgml/api/pgml.train/hyperparameter-search.md)

* [Joint Optimization](open-source/pgml/api/pgml.train/joint-optimization.md)

* [Supervised Learning](open-source/pgml/guides/supervised-learning.md)

* [Natural Language Processing](open-source/pgml/guides/natural-language-processing.md)

* PostgreSQL database, with [_pgml_](/docs/api/sql-extension/), _pgvector_ and many other extensions that add features useful in day-to-day and machine learning use cases

* [PgCat pooler](/docs/product/pgcat/) to load balance thousands of concurrenct client requests across several database instances

We provide a fully managed solution in [our cloud](create-your-database), and document a self-hosted installation in the [Developer Docs](/docs/resources/developer-docs/quick-start-with-docker).

This guide will help you get started with [$100 credits](create-your-database), which includes access to GPU accelerated models and 5 GB of storage, or you can skip to our [Developer Docs](/docs/resources/developer-docs/quick-start-with-docker) to see how to run PostgresML locally with our Docker image.