In bayesian estimate, we have some knowledge about the data/problem (prior). There may be several values of the parameters which explain data and hence we can look for multiple parameters like 5 gammas and 5 lambdas that do this. As a result of the Bayesian Estimate, we get multiple models for making multiple predictions i.e. one for each pair of parameters but with the same prior. So, if a new example needs to be predicted, computing the weighted sum of these predictions serves the purpose.
Maximum likelihood does not take prior into consideration (ignores the prior) so it is like being a Bayesian while using some kind of a flat prior.
