Everything you need for AI Performance in one platform.
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Fundamentals of ML observability
Metrics, feature importance and more
We’re excited ???? to share that Forbes has named Aporia a Next Billion-Dollar Company. This recognition comes on the heels of our recent $25 million Series A funding and is a huge testament that Aporia’s mission and the need for trust in AI are more relevant than ever. We are very proud to be listed […]
Scientific modeling is the aim of which is to make a particular part or feature of the world easier to understand, define, quantify, visualize, or simulate by referencing it to existing and usually commonly accepted knowledge. It requires selecting and identifying relevant aspects of a situation in the real world and then using different types of models for different aims, such as conceptual models to better understand, operational models to operationalize, mathematical models to quantify, and graphical models to visualize the subject. ML models are created by training algorithms on large sets of data with labels or known answers. Then, they’re used to predict labels for new data sets based on patterns found in previous ones.
In Machine Learning/Artificial Intelligence, a model is any system that can make predictions and can be improved through the use of data.
This broad definition is used to to support a large number of use cases in Aporia Models:
Here are some common business use cases: Fraud Detection, Credit Risk, Patient Diagnosis, Churn Prediction, Customer LTV, etc.