Data Science

Collecting and evaluating the data is, however, only part of the challenge. A company’s success hinges on being able to use the knowledge thus gained in their daily operations and to bring data-driven models into production: From Data Science to Production.

We support our customers holistically on their journey from analysis to completed solution. We do this by helping them to create new products which offer real added value to both companies and their customers.

• Analysis platforms which demonstrate the effects of broadcasting TV advertisements on visitors to websites and online stores
• Data science models which can intelligently manage storage capacities and anticipate purchasing behaviour
• Machine-learning models which use archive and real-time data to optimise the system parameters of production equipment
• Recommender systems which create suggestions based on users’ buying and browsing behaviour
• Self-learning analysis models which detect financial fraud methods faster
• Regression models which evaluate quote prices to provide important guidance to users.

These are just some of the solutions we have implemented for our customers. 

We often start out by performing an exploratory analysis and evaluation of all the company’s data and by defining common objectives taking into account its business model.
At inovex, we combine proven academic and scientific analysis methods with many years of experience in IT and software engineering. This enables us to fully explore the opportunities offered by data science with our customers and to create an end-to-end solution which covers everything from data analysis to practical operation and maintenance.
We will assist you in meeting every challenge that arises along the way, from creating complex, scalable analysis methods to integrating them into productive system environments, right through to incorporating feedback mechanisms, security & legal considerations, and changing business models.

This enables us to use tailor-made algorithms to extract valuable information and knowledge from tremendous volumes of data, enabling practical applications to be developed.

For years, inovex has employed a versatile team of highly qualified data scientists and data engineers who make the latest methods of data analysis and machine learning functional for our customers. Our teams have extensive experience in implementing data science products in different industries – from selecting algorithms to integrating models into productive environments and developing them further in live operations. Our experts combine a broad spectrum of scientific data experience in their specific fields – which include biology, mathematics, physics, linguistics, and computer science – with the use of highly efficient algorithms to create our wide range of services.

Our data science team relies on established open-source technologies which enable both high-performance batch processing and the complex real-time analysis of data streams. Appropriate technologies are selected based on the actual requirements for each use case, taking into account the system landscape and the requirements for models and algorithms, as well as any maintenance aspects. Our open source approach enables us to cover all common methods, including regression, decision trees, support-vector machines, and neural networks from a single source.

Some of our technologies:

• Programming languages: Python, R, Java, Scala
• Software libraries: Scikit-learn, Keras, PyTorch, LightFM, Annoy, caret
• Platforms and frameworks: Spark, Flink, Storm, Kafka, Samza, TensorFlow, H2O, Metaflow

We put data science models into production. To ensure that the information from the data collected doesn’t remain merely theoretical, our teams, supplemented by IT engineers, translate the models into practical applications. Sophisticated concepts for integrating our solutions into production environments and for ensuring the maintainability of analyses are decisive factors in our solutions’ success.

Data science projects are perfectly suited to the cloud. While the platform products provided by cloud operators give data scientists a jumpstart when exploring or creating new machine-learning models, there are no one-size-fits-all solutions. Our interdisciplinary teams therefore include cloud engineers to help our customers find their optimal cloud setup and put it into practice.

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Personalisation is an important factor in achieving success in online business. Data science can create recommendation systems which form the technical foundations of personalisation. They allow companies to derive users’ needs from data in order to customise and optimise content.
We have cross-industry experience in the development and operation of recommender systems. We draw upon our many years of practical expertise to work together with our customers in developing custom-tailored solutions to facilitate personalised, effective user experiences.