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Robust Data Representations for Machine Learning

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.

Get baseline ML results on new data rapidly

Your enterprise is full of data that could be used for ML workloads, but chances are you're not using all of it, or maybe even a significant fraction of it. Customers we've worked with have told us all the same story: the work required is high; the cost is  expensive, and so lots of data sits unused.

We all know the best way to improve ML models is to add new data sources, and Featrix lets you explore possible candidate data sets before any heavy lifting in a snap.


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. 

BYO Vector Database

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.

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.