In this talk I will discuss recent work in the UK on developing machine learning approaches to modelling and predicting the yield from National Ignition Facility Inertial Confinement Fusion implosions – one potential pathway to nuclear fusion as an industrial power source. We present several new ensembles of 10^3 -10^4 simulations, showing that the uncertainty on predictions can be accurately decomposed into uncertainty from lack of data, and uncertainty on input parameters. We also show new approaches to finding novel classes of design with comparatively little human intervention. Finally we will briefly discuss how modern data science techniques are being used to support and maximise the utility of other types of HEDP experiments undertaken at the Central Laser Facility at the Rutherford-Appleton Laboratory.