Choosing the right algorithmic model is the key to achieving high accuracy in the forecasting phase. There are hundreds of models on the market, most of them belonging to open source libraries. Verteego offers in its native version the most frequently used algorithms (decision trees, regressions, time series, neural networks, etc.) and makes it easy to "plug in" other libraries. During model training, Verteego automatically compares the different models activated through parameterization files according to prioritization criteria that have been defined by the user.

One of the most time-consuming tasks in the work of the data scientist is the correct selection of the explanatory variables of the model (also called features). It is important to include as many explanatory variables as possible, as these increase the accuracy of the forecasts, without adding superfluous variables that could create unnecessary "noise" and deteriorate the quality of the forecasts. Verteego uses the best techniques for evaluating the relevance of the best performing variables to relieve the user of this sometimes complex task.

Verteego is not a solution for data preparation (like Talend, Trifacta, Dataiku and others). Nevertheless, sometimes it can be convenient to modify input data "on the fly", without having to fully regenerate the underlying datasets.
For this purpose, Verteego allows, very intuitively, to set up preprocessing rules through the settings file. For example, it is possible to generate additional variables, calculated from other variables, to define rules for replacing certain values, to exclude outliers according to certain well-defined criteria, etc.

Your data sets can be very heterogeneous. Depending on the input data sets used in the training, one or the other algorithm may be more efficient. However, it is technically complex to use different algorithms in combination .

With Verteego, this constraint is a thing of the past, so you no longer have to choose between different approaches. Depending on the type of data, Verteego will combine the best modeling approaches for each subset of data to achieve the best overall accuracy.

The predictions generated by Verteego are intended to generate decision making in the organization, either fully automated or driven by humans following the recommendations issued by artificial intelligence. It is then essential that the origin of real decisions is traceable over time.

Verteego keeps in memory all historical models as well as the sets of predictions generated. This allows users to go back on past decisions, understand the parameters of the models used, compare them with each other and possibly reuse them.

Verteego contains a dozen Synapps® models already configured and ready to use. These templates allow you to launch applications without any configuration and to understand the incoming data in order to answer complex business questions in no time at all.

Some examples of pre-configured Synapps® in Verteego : sales forecasting, traffic forecasting, markdown optimization, price optimization, etc.

In forecasting demand, it is essential to be able to differentiate between out-of-stock and low sales in order to obtain accurate forecasts.

Verteego contains a ready-to-use out-of-stock detection processor that uses machine learning to finely understand the correlations in your data in order to estimate whether a value is an out-of-stock or zero or low sales. The annotations you make to your data can then be used as a new input variable to make your prediction models more accurate.

Whether you are looking to forecast your annual revenue, quantities sold in a future fashion collection or hourly traffic in your points of sale or online store, whether you want to generate a single template for all your data or millions of separate templates, one for each possible combination of items and points of sale, Verteego has the right solution for you.

Training and prediction resolutions can easily be specified in the configuration files without having to adapt anything else to the underlying infrastructure.

Verteego was built to work in virtual environments, hosted in the cloud or locally. If you don't want to worry about running Verteego in your own environments, our SaaS solution manages everything for you. Whichever runtime solution you choose, your license will always include all code updates, enhancements and features.

Verteego has been designed to make no difference between a proof-of-concept and a production application. You can test many configurations quickly and inexpensively, and when you are ready for deployment, simply increase the data volume or the scope of your project, because the execution environment will be the same.

Verteego is designed to be fast in all situations. It will use all available resources to reduce the time needed for deployment, model learning and forecasting. And when that work is done, resource consumption slows back down to near zero so you only pay for what you use.

Verteego platform's parallelization technology (based on Kubeflow) allows the use of virtually unlimited computing resources in the cloud to create its learning models. Thanks to the parallelization capabilities employed natively by our technology, Verteego will be able to create accurate models in a decent timeframe, regardless of the volume and complexity of your data.

The confidentiality of your data is a top priority for us. In its SaaS version, we rely on the best practices for securing infrastructures as well as the tools made available through the Google Cloud Platform. Upon request, we would of course be happy to provide you with a detailed description of our security policy and programming practices.