The predictive skill of a neural network model for the prediction of the highly nonlinear Lorenz ’96 dynamics is examined and a way to improve the skill is investigated. We train neural networks with pairs of a large-scale variable and its tendency generated by numerical integrations of full-level Lorenz ’96 equations. The Neural Network (NN) models are then used to estimate the tendency given state of the variable which is then updated without resolving or parameterizing smaller-scale processes. We also apply ensemble data assimilation to the predicted background states and examine to which degree NN models capture the dynamics in a long-term prediction-analysis cycle. It has been found that NN models are skillful in estimating the tendencies for dynamics with quasi-periodic characteristics. Moreover, they have strong potentials in predicting even more chaotic waves when an external forcing has been increased. We have examined if the performance of NN models can be enhanced by using ensemble frameworks in the context of machine learning or training an NN with a dataset generated by ensemble simulations of full-level Lorenz ’96 equations. In these approaches, prediction-analysis cycles run stably for long periods and NN models are skillful in representing the large-scale dynamics. However, NN models can face difficulties in capturing extreme events occurring rarely and whose predictability is very low. An interesting aspect of the results is that efforts need to be focused in finding an effective way to increase the diversity of a whole ensemble and improve the skill in such situations.