Data science and in particular machine learning have developed strongly in recent years.
Machine Learning is used in a number of different business processes and creates the basis for innovation and efficiency.
Machine Learning and Deep Learning are much more than just a technology.
Successful application requires a combination of appropriate expertise from the specialist departments, the modelling approaches from data science and the methods of software engineering and data engineering.
To achieve this, a wide range of challenges can be mastered, from customer engagement and operational optimization to employee empowerment and the transformation of the product landscape.
Data Science focuses primarily on the process of creating models. In order to bring machine learning into productive use, principles from software engineering are required, such as versioning and automated deployment.
Deep Learning deals with neural networks and has made enormous progress in the last 10 years. More and more complex tasks can be solved with this special form of machine learning.
A machine learning project does not run in exactly the same way as a classic software project. The dynamic behaviour of the models and the provision of data must be taken into account.
The field is characterized by a rapid change of tools and frameworks. The cloud acts as a catalyst here.