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fix various typos #117

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fix various typos #117

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2 changes: 1 addition & 1 deletion SUMMARY.md
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Expand Up @@ -23,7 +23,7 @@
* [Workflows](workflows/README.md)
* [Creating a Workflow](workflows/creating-a-workflow.md)
* [Workflow Versions](workflows/workflow-versions.md)
* [Scheduling Workflwos](workflows/managing-workflow-schedules.md)
* [Scheduling Workflows](workflows/managing-workflow-schedules.md)
* [Editing a Workflow](workflows/editing-a-workflow.md)
* [Deleting a Workflow](workflows/deleting-a-workflow.md)
* [Resources](resources/README.md)
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4 changes: 2 additions & 2 deletions metrics-and-checks.md
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Expand Up @@ -2,5 +2,5 @@

In addition to providing a simple API to define and publish workflows, Aqueduct makes it easy for you to monitor your workflows once they have been created. There are two key mechanisms by which you can monitor your workflows:

* [**Metrics**](metrics-and-checks/metrics-measuring-your-predictions.md):allow you to measure your workflows by computing a numerical value over an Artifact ( or other metrics).
* [**Checks**](metrics-and-checks/checks-ensuring-correctness.md):allow you to ensure the correctness of your workflow by taking in one or more Artifacts and Metrics and returning a boolean value indicating the correctness of the workflow.
* [**Metrics**](metrics-and-checks/metrics-measuring-your-predictions.md): allow you to measure your workflows by computing a numerical value over an Artifact (or other metrics).
* [**Checks**](metrics-and-checks/checks-ensuring-correctness.md): allow you to ensure the correctness of your workflow by taking in one or more Artifacts and Metrics and returning a boolean value indicating the correctness of the workflow.
4 changes: 2 additions & 2 deletions quickstart-guide.md
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Expand Up @@ -33,7 +33,7 @@ Note that the API key associated with the server can also be found in the output

### Accessing Data

The base data for our workflow is the [hotel reviews dataset](resources/data-systems/aqueduct-demo-resource.md)) in the pre-built aqueduct\_demo that comes with the Aqueduct server. This code does two things -- (1) it loads a connection to the demo database, and (2) it runs a SQL query against that DB and returns a pointer to the resulting dataset.
The base data for our workflow is the [hotel reviews dataset](resources/data-systems/aqueduct-demo-resource.md) in the pre-built aqueduct\_demo that comes with the Aqueduct server. This code does two things -- (1) it loads a connection to the demo database, and (2) it runs a SQL query against that DB and returns a pointer to the resulting dataset.

```python
demo_db = client.resource("aqueduct_demo")
Expand All @@ -59,7 +59,7 @@ def transform_data(reviews):
'''
This simple Python function takes in a DataFrame with hotel reviews
and adds a column called strlen that has the string length of the
review.
review.
'''
reviews['strlen'] = reviews['review'].str.len()
return reviews
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