With hands-on experience in machine learning, our AI engineers help companies solve complex business problems by facilitating data-based decision making and building new data-driven business models.
We use techniques, including computational intelligence, pattern recognition and predictive analytics to create future-ready machine learning applications.
Every journey begins with the first step. Let our team of highly experienced Process Mining veterans guide you through the rough parts of the road ahead and help you realize maximum ROI with minimal risk.
During this lengthy but critical step, we analyse your data, visualize it for better understanding, potentially select a subset of the most useful data and then pre-process and transform it to create a legitimate dataset.
After that, we split the dataset into three sets of data: training, (cross-) validation and testing sets. This is necessary...
1.) To train a model and define its parameters.
2.) To tweak the model's settings and parameters to achieve the best results.
3.) To evaluate a real model's performance to solve a task after training.
We will train several models to decide which one produces the most accurate results.
We experiment with many different types of models, feature selection, regularization and hyper-parameters tuning until we get a well trained model – neither under- nor overfit. For each experiment, we evaluate model accuracy using the appropriate metric.
The process of putting a model into production depends on your business infrastructure, the volume of data, the accuracy of all previous stages and whether you're using machine learning as a service product.
The project continues even after the model is completed.
We will help you track the metrics and apply testing to define your model’s performance over time and improve it when needed.
With artificial neural network algorithms, inspired by the human brain, we try to learn from large amounts of data.
With statistical algorithms and machine learning techniques, we try to identify the likelihood of future outcomes based on historical data.
With our optimization algorithms we compare iteratively the various solutions until an optimum or a satisfactory solution is found.
Machine learning algorithms help us do identify root causes of specific inefficiencies. With natural language processing we bring together unstructured data with structured ERP data.