zenml-io · strickvl · Jun 17, 2025 · Jun 12, 2025 · Jun 12, 2025 · Jun 12, 2025
diff --git a/assets/main.css b/assets/main.css
@@ -319,7 +319,7 @@ header {
 }
 
 .tutorial-title h2 {
-  font-size: 1rem;
+  font-size: 1.25rem;
   font-weight: 600;
   color: var(--zenml-dark);
   margin: 0;

diff --git a/pipelines/caching/cache_pipeline.py b/pipelines/caching/cache_pipeline.py
@@ -11,9 +11,8 @@
 
 @step(enable_cache=True)
 def slow_step() -> Annotated[int, "answer"]:
-    logger.info("Starting slow computation (3 seconds)...")
+    logger.info("🔄 Actually computing result... (sleeping 3 seconds)")
     time.sleep(3)
-    logger.info("Computation completed!")
     return 42
 
 
@@ -23,10 +22,17 @@ def cache_pipeline():
 
 
 if __name__ == "__main__":
-    logger.info("First run - will take ~3 seconds")
-    cache_pipeline()  # first run ~3 s
-
-    logger.info("Second run - should be instant (cache hit)")
-    cache_pipeline()  # second run instant (cache hit)
+    logger.info("\n" + "="*60)
+    logger.info(">>> RUN 1: First execution (no cache available)")
+    logger.info("="*60)
+    cache_pipeline()
+
+    logger.info("\n" + "="*60)
+    logger.info(">>> RUN 2: Second execution (cache should be used)")
+    logger.info("="*60)
+    cache_pipeline()
+
+    logger.info("\n💡 Notice: The step's log message only appears in Run 1!")
+    logger.info("   In Run 2, the step was skipped entirely due to caching.")
 
     log_dashboard_urls("cache_pipeline")
diff --git a/pipelines/caching/caching.md b/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.
diff --git a/pipelines/completion/completion.md b/pipelines/completion/completion.md
@@ -14,7 +14,7 @@
 
 You Completed the Tutorial
 
-You've successfully completed the **ZenML Interactive Tutorial** and mastered the fundamentals of MLOps with ZenML!
+You've successfully completed the **ZenML Interactive Tutorial** and mastered the fundamentals of using ZenML for MLOps!
 
 </div>
 <div class="right-column">

diff --git a/pipelines/fanOut/fanOut.md b/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!
diff --git a/pipelines/fanOut/fan_pipeline.py b/pipelines/fanOut/fan_pipeline.py
@@ -1,11 +1,3 @@
-"""
-Shows parallel fan-out (multiple identical steps) and a fan-in step that
-gathers all their outputs via the Client API.
-
-Run it once and observe the printed summary.  Re-run: cache is OFF so you
-see the steps execute every time.
-"""
-
 from typing_extensions import Annotated
 from zenml import get_step_context, pipeline, step
 from zenml.client import Client

diff --git a/pipelines/helloWorld/helloWorld.md b/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!
diff --git a/pipelines/metadata/metadata.md b/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!
diff --git a/pipelines/parameters/parameters.md b/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!
diff --git a/pipelines/retries/retries.md b/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.
diff --git a/pipelines/retries/robust_pipeline.py b/pipelines/retries/robust_pipeline.py
@@ -1,8 +1,3 @@
-"""
-Demonstrates automatic retries *and* a failure hook.
-Run it a few times – roughly half will need 1–2 retries.
-"""
-
 import random
 import time
 

diff --git a/pipelines/stepIO/stepIO.md b/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!
diff --git a/pipelines/tagging/tagged_pipeline.py b/pipelines/tagging/tagged_pipeline.py
@@ -1,12 +1,3 @@
-"""
-Artifact Tagging Demonstration
-This example shows different ways to tag artifacts in ZenML:
-1. Regular tags on artifacts using ArtifactConfig
-2. Cascade tags from pipeline to artifacts
-3. Dynamic tagging within steps
-4. Filtering artifacts by tags
-"""
-
 from typing import Annotated
 
 import pandas as pd

diff --git a/pipelines/tagging/tagging.md b/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!
diff --git a/pipelines/visualizations/visualizations-dashboard.png b/pipelines/visualizations/visualizations-dashboard.png
diff --git a/pipelines/visualizations/visualizations.md b/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
@@ -30,6 +26,9 @@ def scatter(df: pd.DataFrame) -> Annotated[HTMLString, "scatter_plot"]:
 - **HTMLString type** lets you embed rich content in the dashboard
 - **Dashboard integration** makes results easily shareable
 
+![Two visualizations generated by this pipeline](./pipelines/visualizations/visualizations-dashboard.png)
+
 ## 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!
diff --git a/pipelines/welcome/welcome.md b/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.
 

diff --git a/utils.py b/utils.py
@@ -19,9 +19,9 @@ def log_dashboard_urls(pipeline_name: str):
     )
     dashboard_url = f"{base_url}/runs/{run.id}"
 
-    logger.info("\n" + "=" * 50)
+    logger.info("\n" + "=" * 60)
     logger.info("✅ Pipeline execution complete!")
     logger.info(f"🌐 View pipeline in dashboard: {base_url}/pipelines/{pipeline_name}")
     logger.info(f"🌐 View this run: {dashboard_url}")
     logger.info(f"🌐 View all artifacts: {base_url}/artifacts")
-    logger.info("=" * 50)
+    logger.info("=" * 60)