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Next-generation mobile communication systems support millimeter Wave (mmWave) transmission and high-mobility scenarios. To cope with propagation environments with unprecedented challenges, data-driven methodologies such as Machine Learning (ML) are expected to act as a fundamental tool for decision support in future mobile systems. However, highquality measurement datasets need to be made available to the research community in order to develop and benchmark ML-based methodologies for next-generation wireless networks. We present a reliable testbed for collecting channel measurements at sub-6GHz and mmWave frequencies. Further, we describe a rich dataset collected using the presented testbed. Our public dataset enables the development and testing of innovative ML-based channel models for both sub-6 GHz and mmWave bands on real-world data. We conclude this paper by discussing promising experimental results on two illustrative ML tasks leveraging on our dataset, namely, channel impulse response forecasting and synthetic channel transfer function generation, upon which we propose future exploratory research directions.