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Small edits (#663)
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@@ -66,11 +66,11 @@ When you access the Dataset, it automatically merges the files from all parent v
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in a fully automatic and transparent process, as if the files were always part of the requested Dataset.
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### Training
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We can now train our model with the **latest** Dataset we have in the system.
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We will do that by getting the instance of the Dataset based on the `latest` tag
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(if by any chance we have two Datasets with the same tag we will get the newest).
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Once we have the dataset we can request a local copy of the data. All local copy requests are cached,
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which means that if we are accessing the same dataset multiple times we will not have any unnecessary downloads.
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You can now train your model with the **latest** Dataset you have in the system, by getting the instance of the Dataset
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based on the `latest` tag
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(if by any chance you have two Datasets with the same tag you will get the newest).
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Once you have the dataset you can request a local copy of the data. All local copy requests are cached,
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which means that if you access the same dataset multiple times you will not have any unnecessary downloads.
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```python
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# create a task for the model training
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@@ -87,7 +87,7 @@ dataset_folder = dataset.get_local_copy()
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## Building the Pipeline
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Now that we have the data creation step, and the data training step, let's create a pipeline that when executed,
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Now that you have the data creation step, and the data training step, create a pipeline that when executed,
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will first run the first and then run the second.
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It is important to remember that pipelines are Tasks by themselves and can also be automated by other pipelines (i.e. pipelines of pipelines).
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