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Small edits (#162)

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pollfly
2022-01-18 13:23:47 +02:00
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parent 8f4851c5c1
commit e72ca23b54
24 changed files with 96 additions and 93 deletions
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2
docs/getting_started/mlops/mlops_first_steps.md
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@@ -115,7 +115,7 @@ Task.enqueue(task=cloned_task, queue_name='default')
```
### Advanced Usage
Before execution, there are a variety of programmatic methods which can be used to manipulate a task object.
Before execution, use a variety of programmatic methods to manipulate a task object.
#### Modify Hyperparameters
[Hyperparameters](../../fundamentals/hyperparameters.md) are an integral part of Machine Learning code as they let you

15
docs/getting_started/mlops/mlops_second_steps.md
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@@ -7,7 +7,10 @@ Pipelines provide users with a greater level of abstraction and automation, with
Tasks can interface with other Tasks in the pipeline and leverage other Tasks' work products.
We'll go through a scenario where users create a Dataset, process the data then consume it with another task, all running as a pipeline.
The sections below describe the following scenarios:
* Dataset creation
* Data processing and consumption
* Pipeline building
## Building Tasks
@@ -56,11 +59,11 @@ dataset.tags = []
new_dataset.tags = ['latest']
```
We passed the `parents` argument when we created v2 of the Dataset, this inherits all the parent's version content.
This will not only help us in tracing back dataset changes with full genealogy, but will also make our storage more efficient,
as it will only store the files that were changed / added from the parent versions.
When we will later need access to the Dataset it will automatically merge the files from all parent versions
in a fully automatic and transparent process, as if they were always part of the requested Dataset.
We passed the `parents` argument when we created v2 of the Dataset, which inherits all the parent's version content.
This not only helps trace back dataset changes with full genealogy, but also makes our storage more efficient,
since it only store the changed and / or added files from the parent versions.
When we access the Dataset, it automatically merges the files from all parent versions
in a fully automatic and transparent process, as if the files were always part of the requested Dataset.
### Training
We can now train our model with the **latest** Dataset we have in the system.