The most advanced ML Observability product in the market
Building an ML platform is nothing like putting together Ikea furniture; obviously, Ikea is way more difficult. However, they both, similarly, include many different parts that help create value when put together. As every organization sets out on a unique path to building its own machine learning platform, taking on the project of building a […]
Start integrating our products and tools.
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 […]
Machine Learning or ML refers to the practice of teaching computers how to learn without explicit programming. According to its name, it gives computers the ability to learn, making them more human-like. Essentially machine learning studies computer algorithms that can improve automatically through experience and by the use of data. It is considered to be a part of artificial intelligence.
A machine learning algorithm builds a model from sample data, known as “training data”, in order to predict events without having been explicitly programmed to do so. There are a wide variety of applications for machine learning algorithms, such as in medicine, email filtering, speech recognition, and computer vision, where conventional algorithms would be difficult or unfeasible to develop.
ML is closely related to computational statistics, which also uses computers to make predictions. While computational statistics are aimed at making predictions about population values, machine learning is aimed at making predictions about individual objects, and their class labels.