diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json
index 17eacbe..48aedb1 100644
--- a/.devcontainer/devcontainer.json
+++ b/.devcontainer/devcontainer.json
@@ -22,7 +22,8 @@
 			},
 			"extensions": 
 			[
-				"betterthantomorrow.calva"
+				"betterthantomorrow.calva",
+				"streetsidesoftware.code-spell-checker"
 			]
 		}
 	},
diff --git a/notebooks/chapters.edn b/notebooks/chapters.edn
index d694282..1b010c0 100644
--- a/notebooks/chapters.edn
+++ b/notebooks/chapters.edn
@@ -9,7 +9,8 @@
   :chapters ["statistics_intro"
              "linear_regression_intro"]}
  {:part "Machine Learning"
-  :chapters ["ml_basic"
+  :chapters ["metamorph"
+             "ml_basic"
              "prepare_for_ml"
              "automl"
              "interactions_ols"]}
diff --git a/notebooks/noj_book/metamorph.clj b/notebooks/noj_book/metamorph.clj
new file mode 100644
index 0000000..00a084d
--- /dev/null
+++ b/notebooks/noj_book/metamorph.clj
@@ -0,0 +1,100 @@
+(ns noj-book.metamorph
+  (:require [clojure.string :as str]))
+
+;; # Machine learning pipelines - DRAFT 🛠
+;; ## Clojure Core Pipelines  
+
+;; Clojure has built-in support for data processing pipelines—a series of functions where the output  
+;; of one step is the input to the next. In core Clojure, these are supported by the so-called  
+;; **threading macro**.  
+
+;; ### Example: Using the Threading Macro  
+
+(-> "hello"
+    (str/upper-case)
+    (str/reverse)
+    (first))
+
+;; In the example above:  
+
+;; 1. `"hello"` is converted to uppercase, resulting in `"HELLO"`.  
+;; 2. The uppercase string is reversed, giving `"OLLEH"`.  
+;; 3. The first character of the reversed string is extracted, which is `\O`.  
+
+;; ## Function Composition with `comp`  
+
+;; We can achieve the same result using **function composition** with `comp`. Note that when using  
+;; `comp`, the order of functions is reversed compared to the threading macro.  
+
+(def upper-reverse-first
+  (comp first str/reverse str/upper-case))
+
+(upper-reverse-first "hello")
+
+;; This defines a function `upper-reverse-first` that:  
+
+;; 1. Converts the input string to uppercase.  
+;; 2. Reverses the uppercase string.  
+;; 3. Extracts the first character.  
+
+;; #### Applying the Composed Function  
+
+;; We can carry the composed function around and apply it in different places:  
+
+(upper-reverse-first "world")
+
+;; Or using `apply`:  
+
+(apply upper-reverse-first ["world"])
+
+;; #### Inlining the Composed Function  
+
+;; We can also inline the composed function without assigning it to a variable:  
+
+((comp first str/reverse str/upper-case) "hello")
+
+;; ## Pipelines in Machine Learning  
+
+;; In machine learning, we usually have two separate concepts:  
+
+;; - **Pre-processing of the data**: Zero or more steps to prepare the data.  
+;; - **Fitting a model**: A single step where the model learns from the data.  
+
+;; Considering these concepts, we aim to create a pipeline that satisfies the following goals:  
+
+;; ### Pipeline Goals  
+
+;; - **Unify Pre-processing and Fitting**: Combine all steps into a single pipeline.  
+;; - **Reusability**: The same pipeline can be executed multiple times (e.g., training vs. prediction),  
+;;   possibly on different data.  
+;; - **Conditional Behavior**: Functions within the pipeline may need to behave differently during  
+;;   training and prediction.  
+;; - **Stateful Steps**: Some steps might need to learn from the data during training and then apply  
+;;   that learned state during prediction.  
+;; - **Readability**: Write pipeline steps in order for easier understanding.  
+;; - **Movability**: The entire pipeline should be assignable to a variable or addable to a sequence,  
+;;   making it modular and reusable.  
+;; - **Callable**: The pipeline should be callable like a function, taking data as input and returning  
+;;   the transformed data.  
+
+;; ### The Need for a New Approach  
+
+;; Clojure's threading macro (`->`) and function composition (`comp`) do not fully meet these requirements  
+;; because:  
+
+;; - They lack the ability to handle state between training and prediction phases.  
+;; - They don't support conditional behavior based on the execution context (e.g., training vs. prediction).  
+;; - They may not represent the pipeline steps in a readable, sequential order when using `comp`.  
+
+;; ## Introducing Metamorph Pipelines  
+
+;; To address these limitations, **Metamorph pipelines** were developed. Metamorph provides a way to  
+;; create pipelines that:  
+
+;; - Compose processing steps in a readable, sequential order.  
+;; - Maintain state between different stages of execution.  
+;; - Allow for conditional behavior within pipeline steps.  
+;; - Can be easily moved, assigned, and called like functions.  
+
+;; *Note: The implementation details of Metamorph pipelines are beyond the scope of this namespace  
+;; but are designed to fulfill the goals outlined above.*  
\ No newline at end of file
diff --git a/notebooks/noj_book/ml_basic.clj b/notebooks/noj_book/ml_basic.clj
index 302d8d3..90fca1f 100644
--- a/notebooks/noj_book/ml_basic.clj
+++ b/notebooks/noj_book/ml_basic.clj
@@ -155,8 +155,9 @@ cat-maps
   [0.0 3.0 0.0 0.0]])
 
 ;; Split data into train and test set
-;;  Now we split the data into train and test. By we use
-;;  a :holdout strategy, so will get a single split in training an test data.
+;;
+;; Now we split the data into train and test. By we use
+;; a :holdout strategy, so will get a single split in training an test data.
 ;;
 (def split
   (first
@@ -165,7 +166,7 @@ cat-maps
 split
 
 ;; ## Train a model
-;; Now its time to train a model.
+;; Now its time to train a model:
 
 (require '[scicloj.metamorph.ml :as ml]
          '[scicloj.metamorph.ml.classification]