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Cloud-based software has many advantages. When services are divided into many independent components, they are easier to update. Also, during peak demand, it is easier to scale cloud services (just hire more CPUs). Hence, many organizations are partitioning their monolithic enterprise applications into cloud-based microservices. Recently there has been much work using machine learning to simplify this partitioning task. Despite much research, no single partitioning method can be recommended as generally useful. More specifically, those prior solutions are "brittle"; i.e. if they work well for one kind of goal in one dataset, then they can be sub-optimal if applied to many datasets and multiple goals. This work extends prior work and proposes DEEPLY to fix the brittleness problem. Specifically, we use (a) hyper-parameter optimization to sample from the Pareto frontier of configurations (b) a weighted loss to choose optimally from this Pareto frontier (c) the 1cycle learning rate policy to avoid local minima with Adam and (d) spectral clustering over k -means. Our work shows that DEEPLY outperforms other algorithms in this space across different metrics. Moreover, our ablation study reveals that of the changes, the weighted loss is the most important, followed by hyper-parameter optimization (contrary to prior belief). To enable the reuse of this research, DEEPLY is available on-line at. • Prior work on automated microservice partitioning does not generalize well. • Hyper-parameter optimization yields a Pareto front of options. • A weighted loss to select from the Pareto front is critical to obtain a good split. [ABSTRACT FROM AUTHOR]