Merel Theisen

Closing this due to inactivity from the author. @hugodscarvalho or anyone else feel free to re-create this PR if you'd like to continue working on it.

As clarified in https://github.com/kedro-org/kedro-viz/issues/2086#issuecomment-2343330747, `kedro-datasets` can continue with the NEP-29 policy and shouldn't affect `kedro-viz`. So we can close this issue and proceed with #818.

Closing this as we've redone the docs completely to MKdocs.

Did https://github.com/kedro-org/kedro/pull/3948 address this enough or do we want to spend more time on improving the docs for namespaces?

@lordsoffallen Do you know about the `kedro catalog resolve` command? https://docs.kedro.org/en/stable/development/commands_reference.html#resolve-dataset-factories-in-the-catalog It basically does what you're describing.

Interesting! When we were doing user research and validation on this feature the use case of resolving in the notebook didn't come up, so we only added the CLI command....

Related: https://github.com/kedro-org/kedro-plugins/issues/849

Hi @anabelchuinard, do you still need help fixing this issue?

Using the `PartitionedDataset` is definitely the recommended Kedro way for batch saving. I've done some digging and it seems that the following lines are causing issues for using the `TensorFlowModelDataset`...

> I think the easiest way with minimal changes will be to add `lazy` argument to `save()` function with `True` default value: > > ```python > def save(self, data: dict[str,...