HI i feel the capability of flexibility of performing pre processing, imputations, cleansing, EDA is still pretty limited. for example: for a missing value imputation, it still has mean, median or mode as a replacement unlike new age methodology like isolation forest method, and if so you still have to perform end to end code in your language of choice.
Also with NAN values :This type of encoding cannot be used in linear models, support vector machines or
neural networks as they expect data to be normalized (or standardized).
We recently were trying to deploy forecasting model using Synapse and AutoML lets say using Prophet. the automl via synapse does not offer extensive hyper parameter tuning nor does it offer Grid Search. Forecasting with deep learning is still pretty limited.