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Actual training of a Featrix embedding space projected to 3 dimensions

Actual training of a Featrix embedding space projected to 3d.

Foundational models built on your data

Featrix offers "embeddings as a service" for structured, tabular data. Now you can ingest CSV, Snowflake, SQL, and other tabular data directly into ML environments for ML workloads without cleaning, imputing, normalizing, and so on.

You can even embed multiple tables into a single space, meaning you no longer have to enrich data before working with it in ML and analytics workloads.

Featrix embeddings are built with deep learning using dozens to hundreds of dimensions to capture the underlying patterns in your data.


Connect data without joining

The best way to improve model accuracy is to add more data to the model that provides additional context. This usually involves doing some kind of join, which is computationally expensive, and when the data requires mapping multiple fields together that don't quite line up, it can turn into a week long project.

Featrix lets you skip over that by training an embedding space with multiple data sources. You can bring together disparate data and create models across the data sets, uncovering insights that are not directly in any single data set.

The power of embedding-based analytics lets us build this capability without the join--not only do you not do the join, but we don't either. This is a huge savings of your time and compute bill of traditional data providers.

Featrix finds deep patterns when comparing the embeddings of two variables during training

In this figure, Featrix uncovers the relationships between two variables during its learning process. These relationships will be preserved and automatically available to your downstream models.

Vectorize the enterprise

Vector embeddings have enabled unstructured data to become easier to work with than structured data. Isn't that crazy? It's time to bring embeddings to structured data in an automated way.

You get paid for insights, not to clean data. It's time to move away from pedantic computing and embrace designs that are robust to noise--because the noise isn't going away.

PyTorch Native

Featrix is built by ML engineers for ML engineers. Use sklearn, torch, numpy, pandas, and all the other tools you already know.

Easy to set up

Run in Docker locally, in AWS, or in our hosted cloud. No matter what your privacy or performance requirements, Featrix delivers.

No POC required

Our open demo notebooks let you try example data or load in your own examples to our service with nothing to download or to install.

Stop guessing which features might work;
focus on models instead

Pretraining on your structured data
Now in alpha release to select customers
Want to try a demo? Get in touch

Pretraining & Fine-Tuning

Do you have strong opinions? You're in luck. Featrix offers tons of flexibility, but you don't have to use it. You can also fine-tune the Featrix embedding models for your own specific needs, as well as train models on a subset of the embedding space.

Multi-modality enrichment

Featrix's vector space enables you to combine embeddings from other systems into one space. You can plug-in other embeddings, use standard open source models, provide your own, and more. Featrix handles all the versioning, semantics, and management of the vector space. 

FAISS Built-in, but you can use others

The jury is still out on which vector database will rule the world, and with so many options, Featrix lets you plugin to whatever you want to use: bring our embeddings to Pinecone, Mivus, faiss, pg_vector, and more. We provide search capabilities powered by FAISS, but integration with other providers is easy.

So Easy to Use Your Boss Could Do It

But maybe we'll keep that between us.


Seriously, you're going to love this.


ML-ready tensors, ready for PyTorch or whatever your favorite ML toolkit is.


Get to work on what actually matters: your models and your business.

"… we changed to the Featrix approach and let our model figure out what matters. [We had] remarkable improvement in the model. And what I like best is we no longer have to hand engineer all these features and track them and so forth."
— Sr Staff Machine Learning Engineer, Fortune 200

Get a demo. Stay in touch.

Data scientist? ML engineer? ML manager? We're all ears. We'd love to show you how we are helping teams cut costs and improve their ML project ROI. Send us an email or drop us a line with this form.