Merge branch 'develop' into feature/modal-step-operator

docs/book/component-guide/experiment-trackers/mlflow.md

@@ -18,17 +18,17 @@ You should use the MLflow Experiment Tracker:
 
 You should consider one of the other [Experiment Tracker flavors](./experiment-trackers.md#experiment-tracker-flavors) if you have never worked with MLflow before and would rather use another experiment tracking tool that you are more familiar with.
 
-## How do you deploy it?
+## How do you configure it?
 
 The MLflow Experiment Tracker flavor is provided by the MLflow ZenML integration, you need to install it on your local machine to be able to register an MLflow Experiment Tracker and add it to your stack:
 
 ```shell
 zenml integration install mlflow -y
 ```
 
-The MLflow Experiment Tracker can be configured to accommodate the following [MLflow deployment scenarios](https://mlflow.org/docs/latest/tracking.html#how-runs-and-artifacts-are-recorded):
+The MLflow Experiment Tracker can be configured to accommodate the following [MLflow deployment scenarios](https://mlflow.org/docs/latest/tracking.html#common-setups):
 
-* [Scenario 1](https://mlflow.org/docs/latest/tracking.html#scenario-1-mlflow-on-localhost): This scenario requires that you use a [local Artifact Store](../artifact-stores/local.md) alongside the MLflow Experiment Tracker in your ZenML stack. The local Artifact Store comes with limitations regarding what other types of components you can use in the same stack. This scenario should only be used to run ZenML locally and is not suitable for collaborative and production settings. No parameters need to be supplied when configuring the MLflow Experiment Tracker, e.g:
+* [Localhost (default)](https://mlflow.org/docs/latest/tracking.html#common-setups) and [Local Tracking with Local Database](https://mlflow.org/docs/latest/tracking/tutorials/local-database.html): This scenario requires that you use a [local Artifact Store](../artifact-stores/local.md) alongside the MLflow Experiment Tracker in your ZenML stack. The local Artifact Store comes with limitations regarding what other types of components you can use in the same stack. This scenario should only be used to run ZenML locally and is not suitable for collaborative and production settings. No parameters need to be supplied when configuring the MLflow Experiment Tracker, e.g:
 
 ```shell
 # Register the MLflow experiment tracker
@@ -38,25 +38,14 @@ zenml experiment-tracker register mlflow_experiment_tracker --flavor=mlflow
 zenml stack register custom_stack -e mlflow_experiment_tracker ... --set
 ```
 
-* [Scenario 5](https://mlflow.org/docs/latest/tracking.html#scenario-5-mlflow-tracking-server-enabled-with-proxied-artifact-storage-access): This scenario assumes that you have already deployed an MLflow Tracking Server enabled with proxied artifact storage access. There is no restriction regarding what other types of components it can be combined with. This option requires [authentication-related parameters](mlflow.md#authentication-methods) to be configured for the MLflow Experiment Tracker.
+* [Remote Experiment Tracking with MLflow Tracking Server](https://mlflow.org/docs/latest/tracking/tutorials/remote-server.html): This scenario assumes that you have already deployed an MLflow Tracking Server enabled with proxied artifact storage access. There is no restriction regarding what other types of components it can be combined with. This option requires [authentication-related parameters](mlflow.md#authentication-methods) to be configured for the MLflow Experiment Tracker.
 
 {% hint style="warning" %}
 Due to a [critical severity vulnerability](https://github.com/advisories/GHSA-xg73-94fp-g449) found in older versions of MLflow, we recommend using MLflow version 2.2.1 or higher.
 {% endhint %}
 
 * [Databricks scenario](https://www.databricks.com/product/managed-mlflow): This scenario assumes that you have a Databricks workspace, and you want to use the managed MLflow Tracking server it provides. This option requires [authentication-related parameters](mlflow.md#authentication-methods) to be configured for the MLflow Experiment Tracker.
 
-### Infrastructure Deployment
-
-The MLflow Experiment Tracker can be deployed directly from the ZenML CLI:
-
-```shell
-# optionally assigning an existing bucket to the MLflow Experiment Tracker
-zenml experiment-tracker deploy mlflow_tracker --flavor=mlflow -x mlflow_bucket=gs://my_bucket --provider=<YOUR_PROVIDER>
-```
-
-You can pass other configurations specific to the stack components as key-value arguments. If you don't provide a name, a random one is generated for you. For more information about how to work use the CLI for this, please refer to the [dedicated documentation section](../../how-to/stack-deployment/README.md).
-
 ### Authentication Methods
 
 You need to configure the following credentials for authentication to a remote MLflow tracking server:
@@ -220,6 +209,6 @@ def step_one(
     ...
 ```
 
-Check out the [SDK docs](https://sdkdocs.zenml.io/latest/integration\_code\_docs/integrations-mlflow/#zenml.integrations.mlflow.flavors.mlflow\_experiment\_tracker\_flavor.MLFlowExperimentTrackerSettings) for a full list of available attributes and [this docs page](../../how-to/use-configuration-files/runtime-configuration.md) for more information on how to specify settings.
+Check out the [SDK docs](https://sdkdocs.zenml.io/latest/integration_code_docs/integrations-mlflow/#zenml.integrations.mlflow.flavors.mlflow_experiment_tracker_flavor.MLFlowExperimentTrackerSettings) for a full list of available attributes and [this docs page](../../how-to/use-configuration-files/runtime-configuration.md) for more information on how to specify settings.
 
 <figure><img src="https://static.scarf.sh/a.png?x-pxid=f0b4f458-0a54-4fcd-aa95-d5ee424815bc" alt="ZenML Scarf"><figcaption></figcaption></figure>

docs/book/how-to/customize-docker-builds/specify-pip-dependencies-and-apt-packages.md

@@ -6,6 +6,8 @@ The configuration for specifying pip and apt dependencies only works in the remo
 
 When a [pipeline is run with a remote orchestrator](../configure-python-environments/README.md) a [Dockerfile](https://docs.docker.com/engine/reference/builder/) is dynamically generated at runtime. It is then used to build the Docker image using the [image builder](../configure-python-environments/README.md#-configure-python-environments) component of your stack.
 
+For all of examples on this page, note that `DockerSettings` can be imported using `from zenml.config import DockerSettings`.
+
 By default, ZenML automatically installs all packages required by your active ZenML stack. However, you can specify additional packages to be installed in various ways:
 
 * Install all the packages in your local Python environment (This will use the `pip` or `poetry` package manager to get a list of your local packages):
@@ -23,6 +25,8 @@ def my_pipeline(...):
 If required, a custom command can be provided. This command must output a list of requirements following the format of the [requirements file](https://pip.pypa.io/en/stable/reference/requirements-file-format/):
 
 ```python
+from zenml.config import DockerSettings
+
 docker_settings = DockerSettings(replicate_local_python_environment=[
     "poetry",
     "export",