We present a new implementation of simulation-based inference using data collected by the ATLAS experiment at the LHC. The method relies on large ensembles of deep neural networks to approximate the exact likelihood. Additional neural networks are introduced to model systematic uncertainties in the measurement. Training of the large number of deep neural networks is automated using a parallelized workflow with distributed computing infrastructure integrated with cloud-based services. We will show an example workflow using the ATLAS PanDA framework integrated with GPU infrastructure from Google Cloud Platform. Numerical analysis of the neural networks is optimized with JAX and JIT. The novel machine-learning method and cloud-based parallel workflow can be used to improve the sensitivity of several other analyses of LHC data.
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