Machine Learning (ML) training is a growing workload in high-performance computing clusters and data centers; furthermore, it is computationally intensive and requires substantial amounts of energy with associated emissions. To the best of our knowledge, previous works in the area of load management have never focused on decreasing the carbon emission of ML training workloads. In this paper, we explore the potential emission reduction achievable by leveraging the iterative nature of the training process as well as the variability of CO2 signal intensity as coming from the power grid. To this end, we introduce two emission-aware mechanisms to shift the training jobs in time and migrate them between geographical locations. We present experimental results on power and carbon emission of the training process together with delay overheads associated with emission reduction mechanisms, for various, representative, deep neural network models. The results show that following emission signals, one can effectively reduce emissions by an amount that varies from 13% to 57% of the baseline cases. Moreover, the experimental results show that the total delay overhead for applying emission-aware mechanisms multiple times is negligible compared to the jobs’ completion time.