With the advent of internet-scale systems, and the need to assure a high functional and non-functional quality of those systems, researchers and practitioners have been working on approaches and tools for monitoring, profiling, and testing of internet-scale systems. One of those approaches is Chaos Engineering, which imposes different challenges for the software reliability engineering community. In this paper, we propose future avenues for research and development with the target of improving chaos engineering capabilities by using machine learning.