Data Science & Machine Learning
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Data Science and machine learning are used to enhance capabilities of business-driven software. Our data scientists and engineers use statistics, mathematics, and cutting edge techniques to build models that collect data and generate actionable insights for businesses to enhance targeting, increase business efficiency, and maximize sales.
PhD Systems and Means of Artificial Intelligence
"Data science is no rocket science if we have data and a clear picture of the problem we are trying to solve. From the very start of a project, data analysis is performed with business goals in mind. Understanding of these goals allows us to analyze data effectively. This is the key to further selection of technologies, the way we build models and make them learn. This is the approach that will enable use of data to generate business value."
"Most ML frameworks—such as Keras and TensorFlow—allow to export models in their specific formats. Another possible approach is ONNX – Open Neural Network eXchange format. It provides interoperability between different open-source ML frameworks and allows to combine various approaches to bring continuous efficiency to the product."
Data science and machine learning solutions can be written in a number of languages, selected in accordance with project specifics.
This machine learning framework has a flexible architecture, while its computations are based on the graph theory and neural networks.
High-level neural networks API, written in Python with a focus on enabling fast experimentation. It is capable of running on top of such tools as TensorFlow, Theano, or CNTK.
This is an open source format for deep learning models. It allows AI developers to form state-of-the-art tool sets throughout a continuous cycle of project evolution.
These are the most popular cloud services, available for training of DS/ML/AI models and further integration. They can either use your custom data or be trained with their own datasets.
A number of frameworks can be used to integrate existing machine learning models with mobile applications.
This comprises 4 main parts: recognition algorithm, data for learning, resources for computation, and integration with software solutions.
These solutions requires time, computational power, and huge amounts of relevant data. In most cases it is reasonable to integrate new products with existing solutions offered by major cloud providers.
Applied Data Science helps retail businesses to increase sales and conduct market research. Models predict customer demands, help optimize pricing, and improve retention policy. Here are some of the features implemented in our latest ERP solution.
Data Science works with face recognition for various purposes, including advanced authentication systems, intruder detection, and identification of customers. These solutions bring secure and convenient experiences of accessing personal data.
Custom solutions can be empowered with personalized recommendations based on statistical methods and machine learning techniques. A number of models were created and applied by our team as support modules for a complex dating application based on psychology of human behavior and relationships.
Augmented and mixed reality devices have already entered the healthcare sector with pioneering software solutions. Now they can be enhanced by means of Data Science and computer vision.