Change lesson numbering to <chapter>-<lesson>.

zenml-io · May 6, 2022 · 2417de2 · 2417de2
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diff --git a/01_Pipelines.ipynb → 1-1_Pipelines.ipynb b/01_Pipelines.ipynb → 1-1_Pipelines.ipynb
@@ -187,7 +187,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "And that's it, we just built our first ML pipeline! Great job!"
+    "And that's it, we just built our first ML pipeline! Great job!\n",
+    "\n",
+    "In the next lesson, `1-2_Artifact_Lineage.ipynb`, you will see one of the coolest features of ML pipelines in action: automated artifact versioning and caching. See you there!"
    ]
   }
  ],

diff --git a/02_Artifact_Lineage.ipynb → 1-2_Artifact_Lineage.ipynb b/02_Artifact_Lineage.ipynb → 1-2_Artifact_Lineage.ipynb
diff --git a/README.md b/README.md
@@ -17,30 +17,30 @@ In the end, you will be able to take any of your ML models from experimentation
 
 ## :teacher: Syllabus
 
-- Chapter 1: ML Pipelines and MLOps Stacks
-    - Lesson 1: ML Pipelines
-    - Lesson 2: Artifact Versioning, Tracking, and Caching
-    - (Lesson 3: Defining MLOps Stacks with ZenML? -> Profiles, Repos)
+- Chapter 1: ML Pipelines
+    - Lesson 1.1: ML Pipelines
+    - Lesson 1.2: Artifact Versioning, Tracking, and Caching
 - Chapter 2: Transparency & Reproducibility
-    - (Lesson 4: Data Validation with DeepChecks / GreatExpectations)
-    - Lesson 5: Experiment Tracking with W&B / MLFlow
-    - Lesson 6: Data Drift Detection with Evidently
-    - Lesson 7: Automated Discord Alerts
-    - (Lesson 8: Feature Stores with Feast?)
+    - Lesson 2.1: Experiment Tracking with W&B / MLFlow
+    - Lesson 2.2: Data Drift Detection with Evidently
+    - Lesson 2.3: Automated Discord Alerts
+    - (Lesson 2.4: Data Validation with DeepChecks / GreatExpectations)
+    - (Lesson 2.5: Feature Stores with Feast?)
 - Chapter 3: Deployment
-    - Lesson 9: Local Deployment & Inference with MLFlow
-    - (Model Serving with Seldon / BentoML?)
-    - Lesson 10: Continuous Deployment based on Data Drift Triggers
-    - Lesson 11: Serverless Deployment with Seldon & Kubeflow
-    - Lesson 12: Serverless Cloud Deployment with Seldon & Kubeflow on AWS (incl. Secret Managers)
-    - (Lesson 13: Running ZenML Steps on Specialized Hardware)
+    - Lesson 3.1: Local Deployment & Inference with MLFlow
+    - Lesson 3.2: Continuous Deployment based on Data Drift Triggers
+    - (Lesson 3.3: Model Serving with Seldon / BentoML?)
+    - Lesson 3.4: Serverless Deployment with Seldon & Kubeflow
+    - Lesson 3.5: Serverless Cloud Deployment with Seldon & Kubeflow on AWS (incl. Secret Managers)
 - Chapter 4: Full Examples
-    - (Lesson 14: Zero to Hero with ZenML - from Experimentation to Production-Grade MLOps)
-    - (Lesson 15: More Examples - zenml example run and ZenFiles)
+    - (Lesson 4.1: Zero to Hero with ZenML - from Experimentation to Production-Grade MLOps)
+    - (Lesson 4.2: More Examples - zenml example run and ZenFiles)
 
 <!--
 - (unused)
     - (Materializers & skipping them)
+    - (Lesson 3: Defining MLOps Stacks with ZenML? -> Profiles, Repos)
+    - (Lesson 3.6: Running ZenML Steps on Specialized Hardware)
 - (missing functionality)
     - Model Registries
     - Explainability Tools
@@ -92,7 +92,7 @@ For some of the advanced lessons you also need to have the following additional
 
 ## :rocket: Getting Started
 
-If you haven't done so already, clone ZenBytes to your local machine. Then, simply use Jupyter Notebook to go through the course lesson-by-lesson, starting with `01_Pipelines.ipynb`:
+If you haven't done so already, clone ZenBytes to your local machine. Then, simply use Jupyter Notebook to go through the course lesson-by-lesson, starting with `1-1_Pipelines.ipynb`:
 
 
 ```bash