Remove h1 headers from tutorial markdown files

Removed the h1 headers and top-level descriptions from all tutorial markdown files to avoid duplication with the tutorial header display in the VSCode extension.

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

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

Author: strickvl

pipelines/caching/caching.md

@@ -1,7 +1,3 @@
-# Caching - Smart Re-runs
-
-Learn how ZenML's caching system can save you time by avoiding redundant computations.
-
 ## What you'll learn
 
 - How to enable caching on steps
@@ -28,4 +24,5 @@ def slow_step() -> Annotated[int, "answer"]:
 
 ## Try it yourself
 
-Run this pipeline twice! The first run takes ~3 seconds, the second is instant thanks to caching.
+Run this pipeline twice! The first run takes ~3 seconds, the second is instant
+thanks to caching.

pipelines/fanOut/fanOut.md

@@ -1,7 +1,3 @@
-# Fan-out/Fan-in - Parallel Processing
-
-Learn how to create parallel workflows that split work across multiple steps and then combine the results.
-
 ## What you'll learn
 
 - How to create parallel processing patterns
@@ -29,4 +25,5 @@ def fan_pipeline(parallel: int = 4):
 
 ## Try it yourself
 
-Run this pipeline to see how it processes data in parallel across multiple steps, then combines the results!
+Run this pipeline to see how it processes data in parallel across multiple
+steps, then combines the results!

pipelines/helloWorld/helloWorld.md

@@ -1,5 +1,3 @@
-# Hello World - Steps & Pipelines
-
 Welcome to your first ZenML pipeline! This tutorial introduces the fundamental concepts of **steps** and **pipelines**.
 
 ## What you'll learn
@@ -30,4 +28,5 @@ def hello_pipeline():
 
 ## Try it yourself
 
-Click the **Run Pipeline** button to execute your first ZenML pipeline and see the output!
+Click the **Run Pipeline** button to execute your first ZenML pipeline and see
+the output!

pipelines/metadata/metadata.md

@@ -1,7 +1,3 @@
-# Metadata - Recording Useful Facts
-
-Learn how to log metadata to track important information about your pipeline runs.
-
 ## What you'll learn
 
 - How to use `log_metadata()` to record key information
@@ -29,4 +25,4 @@ def compute_accuracy() -> Annotated[float, "accuracy_metric"]:
 
 ## Try it yourself
 
-Run this pipeline and check the ZenML dashboard to see the metadata card!
+Run this pipeline and check the ZenML dashboard to see the metadata card!

pipelines/parameters/parameters.md

@@ -1,7 +1,3 @@
-# Parameters - Configurable Behavior
-
-Learn how to make your pipelines configurable with parameters.
-
 ## What you'll learn
 
 - How to add parameters to steps and pipelines
@@ -31,4 +27,4 @@ def param_pipeline(number: int = 3, factor: int = 2):
 
 ## Try it yourself
 
-Run this pipeline to see how parameters control the multiplication operation!
+Run this pipeline to see how parameters control the multiplication operation!

pipelines/retries/retries.md

@@ -1,7 +1,3 @@
-# Retries & Hooks - Robust Pipelines
-
-Learn how to build resilient pipelines that can handle failures gracefully with automatic retries and hooks.
-
 ## What you'll learn
 
 - How to configure automatic retries for flaky steps
@@ -32,4 +28,5 @@ def flaky() -> Annotated[str, "result"]:
 
 ## Try it yourself
 
-Run this pipeline multiple times! Sometimes it succeeds immediately, sometimes it needs retries to handle the random failures.
+Run this pipeline multiple times! Sometimes it succeeds immediately, sometimes
+it needs retries to handle the random failures.

pipelines/stepIO/stepIO.md

@@ -1,7 +1,3 @@
-# Step I/O - Typed Inputs and Outputs
-
-Now let's learn how to pass data between steps with proper type annotations.
-
 ## What you'll learn
 
 - How to define typed step outputs using `Annotated` types
@@ -37,4 +33,5 @@ def count_rows(
 
 ## Try it yourself
 
-Run this pipeline to see how data flows from the `load_data` step to the `count_rows` step!
+Run this pipeline to see how data flows from the `load_data` step to the
+`count_rows` step!

pipelines/tagging/tagging.md

@@ -1,7 +1,3 @@
-# Tagging - Keep Runs Organized
-
-Learn how to use tags to organize and categorize your pipeline runs.
-
 ## What you'll learn
 
 - How to add simple tags to pipelines
@@ -27,4 +23,5 @@ def tagged_pipeline():
 
 ## Try it yourself
 
-Run this pipeline multiple times and watch how the exclusive tag automatically moves to the latest run!
+Run this pipeline multiple times and watch how the exclusive tag automatically
+moves to the latest run!

pipelines/visualizations/visualizations.md

@@ -1,7 +1,3 @@
-# Visualizations - Custom Charts
-
-Learn how to create automatic and custom visualizations that appear in the ZenML dashboard.
-
 ## What you'll learn
 
 - How ZenML automatically visualizes common data types
@@ -32,4 +28,5 @@ def scatter(df: pd.DataFrame) -> Annotated[HTMLString, "scatter_plot"]:
 
 ## Try it yourself
 
-Run this pipeline and check the dashboard to see both the automatic DataFrame visualization and the custom scatter plot!
+Run this pipeline and check the dashboard to see both the automatic DataFrame
+visualization and the custom scatter plot!

pipelines/welcome/welcome.md

@@ -1,7 +1,7 @@
 <div class="two-column-content">
 <div class="left-column">
 
-# Welcome to ZenML Interactive Tutorial!
+# Welcome to the ZenML Interactive Tutorial!
 
 This hands-on tutorial will teach you **ZenML fundamentals** through 10 progressive lessons. Each lesson introduces exactly **one new concept** with copy-paste code examples.