AI and Machine Learning

Use the right tools to the right problem

The ongoing AI revolution has given us a variety of different algorithms, programming languages and frameworks to solve problemsusing data. Which is used for what? Should you use R, Python or Scala? Linear regression, a Gradient Boosting Machine or a Convolutional Neural Network? Do you need to use Spark? Let us find out which solution will work the best for you.


It does not have to be complicated

On the contrary, a simple model works, many times, better than a complicated one. On the contrary, a simple model works many times better than a complicated one. Complicated models certainly have the ability to find connections that a simpler model can not find. On the other hand, there is a risk of finding a connection that does not really exist. Succeeding with AI requires an understanding of what works, as well as an understanding of what risks going wrong.



The end result is always more important than using the latest, most advanced algorithm. Everything is about creating value.


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