Add 5-min quick wins page to docs (#3633)

* Fix link

* Add quick wins guide + photo

* Remove line breaks

* Fix broken slack alerter link

* Fixes based on PR feedback

* Optimised images with calibre/image-actions

* Addressing Marwan's comments

* Handle installed docker

* Add tag filtering image

* Fix deleted text

* Add stack requirements installation to parent Docker image guide

Added instructions to install stack component requirements in the parent Docker image to ensure all required dependencies for the stack are available. This enhances the quick win by ensuring the parent image is fully prepared for all stack components.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

* API & SDK docs fixes

---------

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude <[email protected]>
(cherry picked from commit 9285c4d)

Author: 3 people

RELEASE_NOTES.md

@@ -5518,7 +5518,7 @@ zenml stack update new_default -c local2
 zenml stack describe new_default
 zenml stack remove-component -c
 ```
-More details are in the [CLI docs](https://apidocs.zenml.io/0.7.3/cli/). 
+More details are in the [CLI docs](https://sdkdocs.zenml.io/0.82.0/cli.html). 
 Users can add new stack components to a pre-existing stack, or they can modify 
 already-present stack components. They can also rename their stack and individual stack components.
 

docs/book/.gitbook/assets/pipeline-run-on-the-dashboard.png

@@ -64,6 +64,4 @@ Additionally, we'll need to log in to the container registry so Docker can pull
 docker login
 ```
 
-For more information and a full list of configurable attributes of the `dockerhub` container registry, check out the [SDK Docs](https://apidocs.zenml.io/latest/core_code_docs/core-container_registries/#zenml.container_registries.dockerhub_container_registry.DockerHubContainerRegistry) .
-
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>

docs/book/.gitbook/assets/stack-deployment-options.png

@@ -158,7 +158,7 @@ def profiling_pipeline():
 profiling_pipeline()
 ```
 
-As can be seen from the [step definition](https://apidocs.zenml.io/latest/integration_code_docs/integrations-great_expectations/#zenml.integrations.great_expectations.steps.ge_profiler.great_expectations_profiler_step) , the step takes in a `pandas.DataFrame` dataset, and it returns a Great Expectations `ExpectationSuite` object:
+As can be seen from the step definition, the step takes in a `pandas.DataFrame` dataset, and it returns a Great Expectations `ExpectationSuite` object:
 
 ```python
 @step
@@ -172,8 +172,6 @@ def great_expectations_profiler_step(
     ...
 ```
 
-You can view [the complete list of configuration parameters](https://apidocs.zenml.io/latest/integration_code_docs/integrations-great_expectations/#zenml.integrations.great_expectations.steps.ge_profiler.great_expectations_profiler_step) in the SDK docs.
-
 #### The Great Expectations data validator step
 
 The standard Great Expectations data validator step validates an input `pandas.DataFrame` dataset by running an existing Expectation Suite on it. The validation results are saved in the Great Expectations Validation Store, but also returned as an `CheckpointResult` artifact that is versioned and saved in the ZenML Artifact Store. The step automatically rebuilds the Data Docs.
@@ -219,7 +217,7 @@ def validation_pipeline():
 validation_pipeline()
 ```
 
-As can be seen from the [step definition](https://apidocs.zenml.io/latest/integration_code_docs/integrations-great_expectations/#zenml.integrations.great_expectations.steps.ge_validator.great_expectations_validator_step) , the step takes in a `pandas.DataFrame` dataset and a boolean `condition` and it returns a Great Expectations `CheckpointResult` object. The boolean `condition` is only used as a means of ordering steps in a pipeline (e.g. if you must force it to run only after the data profiling step generates an Expectation Suite):
+As can be seen from the step definition, the step takes in a `pandas.DataFrame` dataset and a boolean `condition` and it returns a Great Expectations `CheckpointResult` object. The boolean `condition` is only used as a means of ordering steps in a pipeline (e.g. if you must force it to run only after the data profiling step generates an Expectation Suite):
 
 ```python
 @step
@@ -232,8 +230,6 @@ def great_expectations_validator_step(
 ) -> CheckpointResult:
 ```
 
-You can view [the complete list of configuration parameters](https://apidocs.zenml.io/latest/integration_code_docs/integrations-great_expectations/#zenml.integrations.great_expectations.steps.ge_validator.great_expectations_validator_step) in the SDK docs.
-
 #### Call Great Expectations directly
 
 You can use the Great Expectations library directly in your custom pipeline steps, while leveraging ZenML's capability of serializing, versioning and storing the `ExpectationSuite` and `CheckpointResult` objects in its Artifact Store. To use the Great Expectations configuration managed by ZenML while interacting with the Great Expectations library directly, you need to use the Data Context managed by ZenML instead of the default one provided by Great Expectations, e.g.:

docs/book/component-guide/container-registries/dockerhub.md

@@ -59,6 +59,7 @@
 
 ## Best practices
 
+* [5-minute Quick Wins](best-practices/quick-wins.md)
 * [Keep Your Dashboard Clean](best-practices/keep-your-dashboard-server-clean.md)
 * [Configure Python environments](best-practices/configure-python-environments.md)
 * [Shared Components for  Teams](best-practices/shared-components-for-teams.md)

docs/book/component-guide/data-validators/great-expectations.md

@@ -23,7 +23,7 @@ The orchestrator then takes over responsibility for executing the pipeline\
 according to the schedule.
 
 {% hint style="info" %}
-For our full reference documentation on schedules, see the [Schedule a Pipeline](https://docs.zenml.io/user-guides/tutorial/managing-scheduled-pipelines) page.
+For our full reference documentation on schedules, see the [Schedule a Pipeline](https://docs.zenml.io/concepts/steps_and_pipelines/scheduling) page.
 {% endhint %}
 
 ### Prerequisites