Machine learning algorithms can do in minutes what would take a human many lifetimes to accomplish. Datacratic’s platform allows real-time machine based decisioning to be integrated into a wide range of applications.
The Datacratic Machine Learning Platform is supported by a complex structure of real-time data flow engineering to ensure the right data gets into the platform at the right time. The system leverages open source, published and proprietary machine learning algorithms and is the foundation for all of Datacratic’s products, services and client solutions.
Built for Real-Time
The platform is designed to support either real-time or batch inputs and outputs (or a hybrid combination) through our productized Datacratic API’s. Datasets can be streamed in real-time or loaded periodically from transactional systems. The Datacratic Machine Learning Platform automatically incorporates and learns from new and updated datasets to ensure solutions built on the platform are always operating with the most current and up to date data.
Reduces Complexity with Intelligent Feature Engineering
Datacratic insulates organizations from the complexities of machine learning. Feature engineering is simplified by smoothing trillions of attributes into shorter, more informative coordinate vectors. Using vector space decomposition algorithms, causal feature vectors are fed into classifiers which are specific to the domain. Classifiers include Bagged Generalized Linear Models, Bagged Boosted Decision Trees, Deep Neural Networks and others.
Unbiased Learning, Adapting & Decisioning
Datacratic’s machine-driven process of learning, adapting and decisioning discovers data points that may be missed by the human eye and eliminates any human bias. Segment and filter control enables the platform to detect and eliminate problematic, biased or irrelevant data and adapt continuously to changing conditions or behaviors. The platform process separates the signal from the noise and uses the past and present data to predict the future and produce decisions which are free from bias.
Hybrid Online and Offline Learning Modes
Models deployed on Datacratic’s platform are designed to operate in a hybrid online and offline learning mode which can also be described as online adaptation and periodic offline training.
Datacratic's platform is not a black box and includes tools to visualize and understand what is driving the behavior and changes in your data. The Data Science Toolkit is currently available to Datacratic customers as part of our beta testing program.
Designed for High Performance & Massive Scalability
The platform is designed to handle hundreds of thousands of events per second and train and adapt to changing conditions in minutes. The proprietary real-time and historical databases are an integral part to Datacratic’s performance based architecture and are designed for massive scalability in secure, virtualized distributed environments. Data can be fed into the platform in Binary JSON, or XML formats enabling flexible integration points for clients to incorporate machine learning into their products and solutions.
Easy Integration with Fully Documented API’s
Apply the full power of the Datacratic Machine Learning Platform to your products and solutions using our fully documented API’s to:
- create intelligent self learning data models and predictions,
- optimize and manage online real time bidding capabilities, and
- personalize and recommend content to give a rich online user experience.