Machine learning industry is expected to peak in the next years. Thanks to a growing availability of big data, connected devices and cloud services, and thanks to the growing democratization of these tools, companies are increasingly adopting them. This comes with an heavy burden on companies, who need to educate themselves and re-structure their IT systems to be able to produce and sustain such systems to make use of them. Which is why more and more players are developing tools allowing an easier integration and utilization of ML-pipelines for commercial use. The goal of this thesis is first to provide a general overview of machine learning industry, its trends, its general direction and what are the drivers and challenges that it is facing and will face in the near future.