Born out of a large-scale collaboration in applied sciences, Heat [1, 2] is an open-source Python library for high-performance data analytics, machine learning, and deep learning. It provides highly optimized algorithms and data structures for tensor computations using CPUs, GPUs and distributed cluster systems. Plus, with Heat, writing scalable scientific and data science applications is as straightforward as using NumPy. With as diverse a user base as, e.g., the Earth System Modeling, neuroscience, and aerospace research communities, Heat offers not only generalized solutions for data-intensive science, but also a platform for ever-expanding cross-discipline collaborations and knowledge transfer. We look forward to many interactions and possible collaborations at AI STAR. [1] Götz, M., Debus, C., Coquelin, et al.: "HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics". 2020 IEEE International Conference on Big Data (Big Data) (pp. 276-287) [2] https://github.com/helmholtz-analytics/heat