The accurate description of the strain rate and temperature dependent response of Aluminium alloys is a perpetual quest in the hot forming industry. In the present study, uniaxial tension, and notched tension experiments are conducted for an aluminium AA7075-T6 sheet metal at various temperatures and strain rates. The experimental campaign covers strain rates ranging from 0.001/s to 100/s, and temperatures ranging from 20°C to 360°C. We observe low strain rate sensitivity at room temperature, with an increase in strain rate sensitivity as temperature is increased up to 360°C. An YLD2000-3D model is employed to describe the anisotropy of the material. A machine learning based hardening model is employed to capture the complex strain rate and temperature effect on the observed hardening response. Counter-example regularization is utilized to guarantee a convergence in the numeric return-mapping algorithm. Comparing the experimental force-displacement curves with the numerical predictions, the neural network model accurately describes the large deformation response of the material in the post-necking range.