In the last decade the rise of data science has been nothing short of spectacular. Today, almost every business has placed a priority on data and analytics in their determination to become data driven. Many companies have undergone what could only be described as a frenzy of activity in this area with different business units buying many different technologies to start developing machine learning models. However, with so many advances in the field of machine learning and data science, we have seen technologies in this area leapfrog each other at a very rapid rate. The result is that many companies now have a broad range of tools and libraries of algorithms scattered around their organisation. For example, some teams may be using Jupyter notebooks and Spark MLlib with Python. Others may be using Tensorflow and Keras. R and R-Studio are also popular as are drop data mining tools from vendors like Knime, SAS, Tibco and Dataiku.

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