The parallel imaging method GRAPPA has been generalized within the Machine Learning framework by introducing the deep-learning method RAKI, in which Convolutional Neural Networks are used for non-linear k-space interpolation. RAKI is a database-free approach that uses scan-specific calibration data. Here, we study the influence of the calibration data on the image quality of 2D imaging sequences. The results indicate that RAKI yields superior signal-to-noise ratio but introduces blurring and loss of detail for typical calibration data amounts at high accelerations. Furthermore, the contrast information in the calibration data must be similar to that of the accelerated scans.