With ever increasing complexity and dynamicity in digital service provider networks, especially with the emergence of 5G, operators seek more automation to reduce the cost of operations, time to service and revenue of new and innovative services, and increase the efficiency of resource utilization, Complex algorithms leveraging ML (machine learning) are introduced, often with the need for frequent training as the networks evolve. Inference is then applied either in the core directly, or in the management stack to trigger actions and configuration changes automatically. This is the essence of Zero Touch. The challenge that analysts are often faced with is to trace back from the inference or prediction to the original events or symptoms that led to the triggered action, which ML model version or pipeline was used. This paper describes the challenges faced by analysts and provides some solutions.