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Reformat pipeline docs (#239)

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pollfly
2022-04-26 13:42:55 +03:00
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docs/getting_started/mlops/mlops_best_practices.md
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@@ -26,7 +26,7 @@ Once we have a Task in ClearML, we can clone and edit its definitions in the UI,
## Advanced Automation
- Create daily / weekly cron jobs for retraining best performing models on.
- Create data monitoring & scheduling and launch inference jobs to test performance on any new coming dataset.
- Once there are two or more experiments that run after another, group them together into a [pipeline](../../fundamentals/pipelines.md).
- Once there are two or more experiments that run after another, group them together into a [pipeline](../../pipelines/pipelines.md).
## Manage Your Data
Use [ClearML Data](../../clearml_data/clearml_data.md) to version your data, then link it to running experiments for easy reproduction.

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docs/getting_started/mlops/mlops_first_steps.md
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@@ -154,7 +154,7 @@ a_numpy = executed_task.artifacts['numpy'].get()
```
By facilitating the communication of complex objects between tasks, artifacts serve as the foundation of ClearML's [Data Management](../../clearml_data/clearml_data.md)
and [pipeline](../../fundamentals/pipelines.md) solutions.
and [pipeline](../../pipelines/pipelines.md) solutions.
#### Log Models
Logging models into the model repository is the easiest way to integrate the development process directly with production.

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docs/getting_started/mlops/mlops_second_steps.md
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@@ -113,4 +113,4 @@ pipe.add_step(
We could also pass the parameters from one step to the other (for example `Task.id`).
In addition to pipelines made up of Task steps, ClearML also supports pipelines consisting of function steps. See more in the
full pipeline documentation [here](../../fundamentals/pipelines.md).
full pipeline documentation [here](../../pipelines/pipelines.md).