Predictive analytics allows to automatically learn from past behavior existing in the data to better predict the future. Predictive models present immediate, objective feedback ensuring consistent operational results and increase the effectiveness of existing processes, giving the ability to reliably forecast behavior.
"Organizations are increasingly recognizing the value that predictive analytics can bring to their businesses. It is our mission to lower the barrier of entry for such algorithms and enable our clients to take advantage of smart, real-time decisions in their day-to-day operations", says Zeller. A doctorate in Physics gives Zeller an edge in the marketplace to provide unique Predictive Analytics solutions and to combine science and software to create superior business and industrial solutions that leverage predictive models in real-time.
Zementis, which started by building predictive models soon realized that their models needed a platform in which they could be easily deployed and managed. From this need, ADAPA, a standard-based, real-time scoring engine came into existence. ADAPA initially supported only neural networks, but it soon became a platform for the deployment of numerous statistical techniques as well as data processing. ADAPA transforms raw data into meaningful feature detectors and post-processes the output of predictive models so that scores are transformed into business decisions.
In the last couple of years ADAPA was launched as a service on the Amazon Cloud and is currently being used worldwide by companies and individuals who want to obtain the most from their predictive models and decision logic.
Zementis enables anyone to access the power of predictive analytics, kicking down the barriers for rapid deployment on big data
ADAPA aids financial institutions merge the power of predictive analytics to take proactive actions to stop abuse as soon as risky patterns are detected. Although real-time analytics provides a vital resource against fraud and abuse, Big Data analytics can provide a much larger context into any prediction. With UPPI (the Universal PMML Plug-in) for Big Data scoring in Hadoop and in-database, clients are able to establish connections between a number of disparate data sources and detect patterns not previously seen.
Traditionally, companies used to take up to six months to deploy their predictive analytic models. In this scenario, whenever the data scientist team finished building the best model out of historical data, it had to be custom coded into production by the IT Engineering team. This lengthy process has no place in the Big Data era, where data is being generated fast and changing rapidly. With the help of PMML, the Predictive Model Markup Language, the traditional model deployment process succumbed and now, models are deployed in minutes. It enables customers to develop different kinds of models, run them very effectively without any manual intervention, while creating a better environment to share model requirements and results.
Zementis enables anyone to access the power of predictive analytics, kicking down the barriers for rapid deployment on Big Data. Predictions will be there at everyone’s fingertips!