Hi @Gabriel , thanks for working with DSDL and sharing your findings here with the community. This behaviour is indeed not what is expected. Let me ask a few question and share some thoughts to find a way to resolve this issue. 1. Did you implement the def load() and def save() in your notebook? If so, how did you serialise the KMeans model object (which would contain the trained state after running your fit). 2. MLTK's fit and apply commands have some specific behaviour: fit is an eventing command, apply is a stateful streaming command. But if your model is properly loaded you should not see this deviation. 3. While I appreciate to see you implementing in DSDL, you probably know that MLTK has KMeans out of the box and might be easier to use to achieve the same goal. Would this be an alternative? 4. For DSDL you can also consider opening a support case for this issue. If your notebook and sample data is anonymised and contains no sensitive information, you could share it so we can reproduce your issue. Hope this is helpful for you. Please let us know!
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