Reflect Deepchecks integration with ZenML and SDK updates

Author: htahir1

docs/book/component-guide/data-validators/deepchecks.md

@@ -50,7 +50,7 @@ The ZenML integration restructures the way Deepchecks validation checks are orga
 * **model validation checks** require a single dataset and a mandatory model as input. This list includes a subset of the model evaluation checks provided by Deepchecks [for tabular data](https://docs.deepchecks.com/stable/tabular/auto_checks/model_evaluation/index.html) and [for computer vision](https://docs.deepchecks.com/stable/vision/auto_checks/model_evaluation/index.html) that expect a single dataset as input.
 * **model drift checks** require two datasets and a mandatory model as input. This list includes a subset of the model evaluation checks provided by Deepchecks [for tabular data](https://docs.deepchecks.com/stable/tabular/auto_checks/model_evaluation/index.html) and [for computer vision](https://docs.deepchecks.com/stable/vision/auto_checks/model_evaluation/index.html) that expect two datasets as input: target and reference.
 
-This structure is directly reflected in how Deepchecks can be used with ZenML: there are four different Deepchecks standard steps and four different [ZenML enums for Deepchecks checks](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-deepchecks.html#zenml.integrations.deepchecks) . [The Deepchecks Data Validator API](deepchecks.md#the-deepchecks-data-validator) is also modeled to reflect this same structure.
+This structure is directly reflected in how Deepchecks can be used with ZenML: there are four different Deepchecks standard steps and four different [ZenML enums for Deepchecks checks](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-deepchecks.html) . [The Deepchecks Data Validator API](deepchecks.md#the-deepchecks-data-validator) is also modeled to reflect this same structure.
 
 A notable characteristic of Deepchecks is that you don't need to customize the set of Deepchecks tests that are part of a test suite. Both ZenML and Deepchecks provide sane defaults that will run all available Deepchecks tests in a given category with their default conditions if a custom list of tests and conditions are not provided.
 
@@ -119,7 +119,7 @@ The integration doesn't yet include standard steps for computer vision, but you
 All four standard steps behave similarly regarding the configuration parameters and returned artifacts, with the following differences:
 
 * the type and number of input artifacts are different, as mentioned above
-* each step expects a different enum data type to be used when explicitly listing the checks to be performed via the `check_list` configuration attribute. See the [`zenml.integrations.deepchecks.validation_checks`](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-deepchecks/#zenml.integrations.deepchecks.validation_checks) module for more details about these enums (e.g. the data integrity step expects a list of `DeepchecksDataIntegrityCheck` values).
+* each step expects a different enum data type to be used when explicitly listing the checks to be performed via the `check_list` configuration attribute. See the [`zenml.integrations.deepchecks.validation_checks`](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-deepchecks.html) module for more details about these enums (e.g. the data integrity step expects a list of `DeepchecksDataIntegrityCheck` values).
 
 This section will only cover how you can use the data integrity step, with a similar usage to be easily inferred for the other three steps.
 
@@ -151,7 +151,7 @@ def data_validation_pipeline():
 data_validation_pipeline()
 ```
 
-As can be seen from the [step definition](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-deepchecks/#zenml.integrations.deepchecks.steps.deepchecks_data_integrity) , the step takes in a dataset and it returns a Deepchecks `SuiteResult` object that contains the test results:
+As can be seen from the step definition, the step takes in a dataset and it returns a Deepchecks `SuiteResult` object that contains the test results:
 
 ```python
 @step

docs/book/component-guide/step-operators/spark-kubernetes.md

@@ -211,7 +211,7 @@ When you want to run your steps on a Kubernetes cluster, Spark will require you
 When using Spark in EKS, you need to use the latter and utilize the `docker-image-tool`. However, before the build process, you also need to download the following packages
 
 * [`hadoop-aws` = 3.3.1](https://hadoop.apache.org/docs/r3.4.1/hadoop-aws/tools/hadoop-aws/index.html)
-* [`aws-java-sdk-bundle` = 1.12.150](https://mvnrepository.com/artifact/com.amazonaws/aws-java-sdk-bundle)
+* [`aws-java-sdk-bundle` = 1.12.150](https://javadoc.io/doc/com.amazonaws/aws-java-sdk-bundle/latest/index.html)
 
 and put them in the `jars` folder within your Spark installation. Once that is set up, you can build the image as follows:
 

docs/book/how-to/debug-and-solve-issues.md

@@ -115,7 +115,7 @@ Read more about how to set environment variables for:
 
 * For [Linux](https://www3.ntu.edu.sg/home/ehchua/programming/howto/Environment_Variables.html#zz-3./).
 * For [macOS](https://youngstone89.medium.com/setting-up-environment-variables-in-mac-os-28e5941c771c).
-* For [Windows](https://www.computerhope.com/issues/ch000549.htm).
+* For [Windows](https://www3.ntu.edu.sg/home/ehchua/programming/howto/Environment_Variables.html).
 
 ### Client and server logs
 

docs/book/how-to/infrastructure-deployment/stack-deployment/README.md

@@ -56,7 +56,7 @@ Here's an approach you can take that is a good balance between convenience and s
 If you follow this approach, you can keep your data scientists free from the hassle of figuring out the best authentication mechanisms for the different cloud services, having to manage credentials locally, and keep your cloud accounts safe, while still giving them the freedom to run their experiments in the cloud.
 
 {% hint style="info" %}
-Please note that restricting permissions for users through roles is a ZenML Pro feature. You can read more about it [here](https://docs.zenml.io/pro/heirarchy/roles). Sign up for a free trial here: https://cloud.zenml.io/.
+Please note that restricting permissions for users through roles is a ZenML Pro feature. You can read more about it [here](https://docs.zenml.io/pro/core-concepts/roles). Sign up for a free trial here: https://cloud.zenml.io/.
 {% endhint %}
 
 ## How to deploy and manage stacks

docs/book/how-to/model-management-metrics/track-metrics-metadata/fetch-metadata-within-pipeline.md

@@ -45,7 +45,7 @@ def my_pipeline():
 ```
 
 {% hint style="info" %}
-See the [SDK Docs](https://sdkdocs.zenml.io/latest/core_code_docs/core-new/#zenml.pipelines.pipeline_context.PipelineContext) for more information on which attributes and methods the `PipelineContext` provides.
+See the [SDK Docs](https://sdkdocs.zenml.io/latest/index.html#zenml.pipelines.PipelineContext) for more information on which attributes and methods the `PipelineContext` provides.
 {% endhint %}
 
 <!-- For scarf -->

docs/book/how-to/model-management-metrics/track-metrics-metadata/fetch-metadata-within-steps.md

@@ -37,7 +37,7 @@ def my_step():
 ```
 
 {% hint style="info" %}
-See the [SDK Docs](https://sdkdocs.zenml.io/latest/core\_code\_docs/core-new/#zenml.steps.step\_context.StepContext) for more information on which attributes and methods the `StepContext` provides.
+See the [SDK Docs](https://sdkdocs.zenml.io/latest/index.html#zenml.steps.StepContext) for more information on which attributes and methods the `StepContext` provides.
 {% endhint %}
 
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>