Unlike Xyggy, machine learning algorithms are designed for batch-learning which means the data model must be re-trained as new data becomes available.

Because all data is not in production use, there is a continuous loss of business opportunity. This is on top of re-training, which is an expensive recurring cost in both resources and time.

In a realtime digital world, batch-learning is counter-intuitive.

The scalable Xyggy Engine infers in realtime from all data – current and new. Data can be added, updated and deleted in realtime.

From a query of items, the Engine returns other similar items in ranked order (using Bayesian supervised clustering). With the dynamic API, build interactive machine intelligence applications to improve internal efficiency or competitive advantage. The deployment and maintenence cycle is easier, faster and cheaper.

In a realtime digital world, you need realtime machine intelligence.

There is a lot more to the Engine's capability and these presentations will get you upto speed:

Start with the Engine overview followed by the Use-cases. A walk through the short Tutorial is highly encouraged and keep the API information handy as you do.

Anomaly Detection is an important application area for machine learning and we have chosen images as the illustrative example to identify outliers.

For the first time, machines are making predictions and it signals the start of a momentous cultural shift. Driving this forward are machine learning algorithms with data as the fuel.

On a parallel path, the growing ubiquity of mobile devices is leading to a convergence between the physical analog world and the digital world.

The Xyggy purpose is very simple:

To make it easier and faster for developers to deploy and maintain realtime scalable and interactive machine learning applications on a secure fault-tolerant cloud

To demonstrate massively scalable and realtime machine learning, we ran Benchmarks on a large distributed system at the Cambridge University High Performance Computing Center.

Two key operations were timed - query execution and adding new items - as the number of data items and non-zeros increase across a rising number of cores from 16 to 1024.

Download the Free Trial Version and start to prototype machine intelligence applications.

Along with the Get Started, review the Tutorial and the API information.

If you already have feature engineered data then you'll be up and running in no time. Give it a go - it really is so much easier and faster. With the dynamic API, queries can be modified between invocations to deliver interactive applications.

If you have questions or want to know more then please get in touch:

Email Hello Xyggy, or at the Xyggy Google Group.