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Machine Learning

The time is ripe for Machine Learning projects

[] Machine Learning

Machine Learning (ML) presents companies with a great deal of potential for influencing and optimising their business and operating models. Currently ML is experiencing a real boom, because previous technical limitations are now a thing of the past. Today it is possible...

  • to access large amounts of data for training learning algorithms
  • to use powerful GPUs to dramatically speed up computing operations
  • to consult free ML software libraries, cloud technologies, distributed computing architectures and in-memory databases.

Companies win with Machine Learning

When adopting ML, many companies tend to create projects that are too large, too technical and too complex. There are a whole series of applications that are relatively easy to implement and whose business benefits are immediately apparent. Most Machine Learning algorithms can be used for the following traditional areas of application:

  • Fraud detection
  • Email classification and spam detection
  • Diagnostic systems
  • Content personalisation
  • Customer churn forecasting
  • Automated solution recommendations for customer service
  • Sentiment analyses (e.g. positive / negative opinions)
  • Routing of messages
  • Analysis of up-selling opportunities
  • Recommender systems


Whether you have been working with Machine Learning for a long time or are just looking to get started, we offer business decision-makers and project managers independent and well-founded advice on all aspects of ML. We try to make the very dynamic and confusing ML market, its associated technologies and related application areas a bit more transparent for you.

Machine Learning in practice: video in German

Rather than being theoretical, the following video provides a concrete use case from actual production practice (predictive maintenance). We show you how you can …

  • drastically save time in preparing data – data scientists spend 80% of their project time on this
  • close the gap between development and go-live – operationalisation of predictive analytics models
  • implement Machine Learning and visualisation using one single tool

From data mining to visualisation, all steps of an ML project are presented. You will get a short overview of ML, see a detailed use case demonstration and get helpful recommendations for implementing your ML project.

Content of the video:

  1. Intro: Machine Learning areas of application and their business value
  2. Practical demo: Predictive analytics for production
  3. Recommendations for project implementation

    Dr. David James
    Data Scientist

    Please get in touch with me if you're planning a Machine Learning project, are looking for a solution platform or are interested in a practical implementation. I'll show you how to take advantage of the opportunities and how to avoid the various pitfalls.


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    Pentaho from Hitachi Vantara is the leading software for data integration and big data analytics. Hitachi Vantara offers a comprehensive solution portfolio for Big Data, Internet of Things and Cloud. it-novum is Hitachi Vantara's Big Data Insights and IoT partner and the largest Pentaho implementation and training partner in EMEA.