Deprecate model deployer in docs (#4076)

* Add deprecation notice to Model Deployer docs

Add a prominent deprecation notice to the Model Deployer documentation
explaining that it has been deprecated in favor of the more flexible
Deployer component and Pipeline Deployments feature.

The notice explains:
- Why Model Deployer is deprecated (focused on single-model serving
  vs. modern multi-step pipeline needs)
- Benefits of the new Pipeline Deployment approach (unified, flexible,
  simpler, more extensible)
- Clear migration path for users
- Links to relevant documentation

This aligns with the evolution toward pipeline deployments as described
in the recent blog post about real-time AI pipelines.

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

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

* Use absolute URLs in Model Deployer deprecation notice

Update the deprecation notice to use absolute docs.zenml.io URLs
instead of relative file paths:
- https://docs.zenml.io/component-guide/deployers
- https://docs.zenml.io/how-to/deployment

This ensures the links work correctly when viewed on the
documentation website.

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

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

* Add Model Deployer deprecation guidance to tutorial and LLMOps docs

Users following the hyperparameter tuning tutorial and LLMOps finetuning
guide were being directed toward the deprecated Model Deployer component
without awareness of the newer Pipeline Deployments approach.

Added minimal, focused notices at decision points where users choose
deployment strategies, guiding them toward Pipeline Deployments while
acknowledging Model Deployer remains available for backward compatibility.

* Update docs/book/component-guide/model-deployers/README.md

* Apply suggestions from code review

---------

Co-authored-by: Claude <[email protected]>
Co-authored-by: Alex Strick van Linschoten <[email protected]>

Author: 3 people

docs/book/component-guide/model-deployers/README.md

@@ -5,6 +5,23 @@ description: Deploying your models and serve real-time predictions.
 
 # Model Deployers
 
+{% hint style="warning" %}
+**DEPRECATION NOTICE**
+
+The Model Deployer stack component is deprecated in favor of the more flexible [**Deployer**](https://docs.zenml.io/stacks/stack-components/deployers) component and [**Pipeline Deployments**](https://docs.zenml.io/concepts/deployment).
+
+The Model Deployer abstraction focused exclusively on single-model serving, but modern ML workflows often require multi-step pipelines with preprocessing, tool integration, and custom business logic. The new Pipeline Deployment paradigm provides:
+
+- **Unified approach**: Deploy any pipeline—classical ML inference, agentic workflows, or hybrid systems—as a long-running HTTP service
+- **Greater flexibility**: Customize your deployment with full FastAPI control, add middleware, custom routes, and even frontend interfaces
+- **Simpler mental model**: One primitive for all deployment scenarios instead of separate abstractions for models vs. pipelines
+- **Better extensibility**: Deploy to Docker, AWS App Runner, GCP Cloud Run, and other platforms with consistent patterns
+
+**Migration Path**: Instead of using Model Deployer-specific steps, wrap your model inference logic in a regular ZenML pipeline and deploy it using `zenml pipeline deploy`. See the [Pipeline Deployment guide](https://docs.zenml.io/concepts/deployment) for examples of deploying ML models as HTTP services.
+
+While Model Deployer integrations remain available for backward compatibility, we strongly recommend migrating to Pipeline Deployments for new projects.
+{% endhint %}
+
 Model Deployment is the process of making a machine learning model available to make predictions and decisions on
 real-world data. Getting predictions from trained models can be done in different ways depending on the use case, a
 batch prediction is used to generate predictions for a large amount of data at once, while a real-time prediction is

docs/book/user-guide/llmops-guide/finetuning-llms/deploying-finetuned-models.md

@@ -40,6 +40,10 @@ When choosing a deployment option, consider factors such as your team's expertis
 
 ## Deployment with vLLM and ZenML
 
+{% hint style="info" %}
+**Note**: The example below uses the Model Deployer component, which is maintained for backward compatibility. For new projects, consider using [Pipeline Deployments](https://docs.zenml.io/concepts/deployment) which offer greater flexibility for deploying LLM inference workflows with custom preprocessing and business logic.
+{% endhint %}
+
 [vLLM](https://github.com/vllm-project/vllm) is a fast and easy-to-use library
 for running large language models (LLMs) at high throughputs and low latency.
 ZenML comes with a [vLLM integration](https://docs.zenml.io/stacks/model-deployers/vllm)

docs/book/user-guide/tutorial/hyper-parameter-tuning.md

@@ -118,5 +118,7 @@ For a deeper exploration of how to query past pipeline runs, see the [Inspecting
 ## Next steps
 
 * Replace the simple grid‑search with a more sophisticated tuner (e.g. `sklearn.model_selection.GridSearchCV` or [Optuna](https://optuna.org/)).
-* Serve the winning model via a [Model Deployer](https://docs.zenml.io/stacks/model-deployers) to serve it right away.
-* Move the pipeline to a [remote orchestrator](https://docs.zenml.io/stacks/orchestrators) to scale out the search.
+* Deploy the winning model as an HTTP service using [Pipeline Deployments](https://docs.zenml.io/concepts/deployment) (recommended) or via the legacy [Model Deployer](https://docs.zenml.io/stacks/stack-components/model-deployers).
+* Move the pipeline to a [remote
+  orchestrator](https://docs.zenml.io/stacks/orchestrators) to scale out the
+  search.