Predictive Power
Rapidly Assess Data for Predictive AI
Explore the AI possibilities rapidly
Traditional Data Analysis is Expensive
We've heard the stories over and over at all kinds of customers. AI return on investment is extremely tricky. Why? The up-front cost of preparing a data set for predictive AI is expensive and can take weeks or months to sort out.
Featrix Enables Rapid Assessment with Controlled Costs
With Featrix, you can get started on a new data set right away. Just upload it to Featrix and you can begin easy analysis on whether the data set contains sufficient information to build AI models. Just create a few neural functions and check their behavior and you're off to the races.
No data cleaning steps
Featrix works with raw data out of the box. We automate all the encoding and training so you don't have to worry about it. Just pick a target and a budget and we'll see what we can do.
Tools for success at every skill level
Our goal is to enable a developer of any skill level to build powerful predictive AI applications.
But we also offer a big toolbox for the experienced data scientist: you can query and sample our embedding space to build similarity plots across any target variable pairs and our automated embedding spaces under the hood can reveal complex relationships that might otherwise be difficult to uncover and require days or weeks of coding. With Featrix, these projects become tasks and you can get results in an afternoon.
Inter-Relationships
Let's consider a disjoint data set that describes pet types (cat or dog) with the height and weight of the animal. Unfortunately, the vet collecting the data had some untrained interns doing the measurement and they forgot to use a shared spreadsheet. So the ended up with two data files: one relating type and height, and one relating weight and height.
But we want to examine the relationship between pet type and weight. With Featrix, we can create a foundational embedding space on these two data sets without every joining them, and then examine the relationships across all combinations of variables.
Intra-Relationships
As another example, we are looking at the Boston 311 data set here. The 311 data set is a set of non-emergency tickets that citizens can open with the city. These tickets include things like noise complaints, garbage cans in the street, broken street lights, and so on.
From looking at the relationship between the day of the week (vertical axis) and the time of day (horizontal axis), we can see there is a cluster during the work week and business hours--these tickets are related to each other. We can also see the weekend evenings have a different cluster emerging.
The training on this data takes just minutes and exploring the relationships is nearly instant.
Working with us
We make it easy to work with us. If you would like to partner with Featrix, contact hello@featrix.ai.
Featrix: Now any developer can build powerful machine learning solutions using the latest techniques with a simple API.
No data preparation. No feature engineering. No hyper-parameter tuning. No unexpected cloud bills.
With Featrix, a few API calls are all you need to build elite-level machine learning without all the complexity.