Žiga Vaupot

Traditionally, the use of machine learning is in the domain of the data scientist. The latter has in-depth knowledge of machine learning methods and algorithms and strives for the most optimal preparation of machine learning models, which are then used for various analyses such as predictions, customer segmentation, anomaly detection, finding patterns and the like. Because the machine learning process usually takes a long time, even a few weeks, the data scientist is a rather “rare species”. Business users usually need results quickly and often don’t have time to wait their turn. They are faced with the dilemma to choose between lower accuracy over the speed of development of machine learning models.

There is no doubt that there are business problems which require machine learning models that provide accuracy as high as possible, but on the other hand there are situations where speed of finding an advanced analytics solution is even more important and when “good enough” model is acceptable to the business.

Regardless of the dilemma described above, one thing is in common to both approaches. It is the machine learning process which must be implemented in any case. It is now commonly expected that this is supported by any analytics tool. Oracle Analytics certainly fulfills this requirement.

The workshop Business Users and Machine Learning in Oracle Analytics will look at not only a few examples where a business user can use machine learning algorithms, but also what options are available for the whole machine learning process and how they can use them independently, without the involvement of data scientists and/or other IT professionals. Of course, we will also look at scenarios when data scientists are available and how the results of their work can also be used in business analysis.