Continuous Machine Learning SolutionRequest A Demo
Traditional machine learning uses historical data to uncover patterns and create predictive signals. When environments are stable and data is static, this works quite well.
However, today’s dynamic, hyper-connected world is anything but stable, and the most useful data is rarely static. The ever-increasing volume and velocity of data requires technology that can adapt in real-time and drive solutions that continuously deliver measurable value.
SymetryML brings a level of speed, privacy and efficiency that is unmatched by traditional machine learning solutions.
SymetryML’s Proprietary Data Structure
SymetryML’s continuous machine learning techniques can update a trained model when each new data point occurs. This allows a model to not only predict, but learn in real-time as well. In other words, there’s no need to store a historical training set and retrain a model periodically, the model can stay up-to-date.
To accomplish this, SymetryML uniquely decouples learning a model, from the actual data by extracting key statistical values and creating a Proprietary Statistical Representation (PSR) of your entire data set. This compression-like technique provides several unique benefits, including rapid model building, continuous retraining and lowers overall compute costs and resources.
Explore The SymetryML Solution
Through the UI or REST API, users can easily and rapidly build online machine learning models to explore ideas and put models into production faster and easier than ever before.learn more
Bring privacy to the forefront with a distributed computing framework that preserves privacy and eliminates the need to aggregate data for model building & analytics.learn more
Learn how SymetryML helped a hedge fund increase their win percentage by 20%learn more