First Look - Yottamine

I met the folks from Yottamine at Predictive Analytics World and got a chance to get a demo and an update recently. ...

The software itself is web-based, allowing data files to be loaded up into one of many folders. The dataset is a standard flat analytic dataset that can contain numbers (treated as continuous unless the user specifies them as discrete), strings (treated as discrete), dates etc. Clients can upload CSV files or ARFF files (WEKA format)

Once loaded the client can specify the target column - what it is they want to predict - and whether it is a regression or classification problem. The software can separate data into training and testing data and allows the customer to specify different testing approaches including uploading a separate test data set.

Yottamine's software then creates a suitable EC2 cluster depending on the amount of data uploaded for building the predictive model. The algorithms Yottamine have implemented are highly parallelized, allowing them to take advantage of the largest clusters available. They claim increased accuracy for their modeling approach over standard ones as well as improved performance thanks to their ability to scale out to large clusters. The modeling techniques supported include Linear- Polynomial-and Gaussian-Support Vector Machines as well as Local Model (based on a K Nearest Neighbor/SVM combination). Local Model can achieve equal or better accuracy than already highly accurate SVMs model. ...

Yottamine Predictive Platform is going to be attractive to modelers who know how to create the right analytic data set to feed into a model and who want to use scalable cloud resources to build their final model. The platform claims to be the first on-demand model-building platform that charges clients based on their hourly usage instead of paying a fixed subscription fee per month. A free trial of the platform for 15 days is available by applying here.

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