An increasing number of companies are using data analytics to improve their products, services, and business processes. However, learning knowledge effectively from massive data sets always involves nontrivial computational resources. Most businesses thus choose to migrate their hardware needs to a remote cluster computing service (e.g., AWS) or to an in-house cluster facility which is often run at its resource capacity. In such scenarios, where jobs compete for available resources utilizing resources effectively to achieve high-performance data analytics becomes desirable. Although cluster resource management is a fruitful research area having made many advances (e.g., YARN, Kubernetes), few projects have investigated how further optimizations can be made specifically for training multiple machine learning (ML) / deep learning (DL) models. In this work, we introduce FlowCon, a system which is able to monitor loss functions of ML/DL jobs at runtime, and thus to make decisions on resource configuration elastically. We present a detailed design and implementation of FlowCon, and conduct intensive experiments over various DL models. Our experimental results show that FlowCon can strongly improve DL job completion time and resource utilization efficiency, compared to existing approaches. Specifically, FlowCon can reduce the completion time by up to 42.06% for a specific job without sacrificing the overall makespan, in the presence of various DL job workloads.