Prediction using Machine Learning (ML) techniques on Big Data is a computationally and system-wide challenging problem. Especially in the case when the system is processing approximately 10^9 observations per day scalability is the prime concern. In order to be able to rapidly train models covering whole multivariate space the time series vectors, which exhibit significant similarities, are clustered into the groups. Consequently the resulting vector clusters could be modelled using ML tools capable of coefficient estimation at the massive scale (Apache Spark with Scikit Learn). Presentation describes application of the Linear Regression and Support Vector Regression with Radial Basis Function kernel. This approach enables training models fast enough to complete the task within a couple of hours, allowing daily or even real time updates of the coefficients. The above machine learning framework is used to predict the airfares used as support tool for the Revenue Management systems.