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Practice

The Machine Learning Workflow


A machine learning workflow is a structured process that guides how we move from a raw dataset to a deployed, working model.
Following a clear workflow ensures efficiency, reproducibility, and better results.

Rather than listing each stage here, take a look at the whiteboard diagram for a visual breakdown of the workflow steps and their relationships.


Example: Simple Workflow in Scikit-learn​

ML Workflow Example: Classification
# Install scikit-learn in Jupyter Lite
import piplite
await piplite.install('scikit-learn')

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 1. Load dataset
iris = load_iris()
X, y = iris.data, iris.target

# 2. Split into train/test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# 3. Choose model
model = KNeighborsClassifier(n_neighbors=3)

# 4. Train model
model.fit(X_train, y_train)

# 5. Evaluate
predictions = model.predict(X_test)
acc = accuracy_score(y_test, predictions)

print(f"Accuracy: {acc:.2f}")

This example demonstrates the core loop of the ML workflow:

  • Data preparation
  • Model selection
  • Training
  • Evaluation

Key Takeaways​

  • A well-structured ML workflow reduces errors and improves reproducibility.
  • The steps are iterative — you might return to earlier stages if performance isn’t satisfactory.
  • Scikit-learn provides tools for almost every stage, from preprocessing to evaluation.

What’s Next?​

In the next lesson, we’ll dive into Supervised vs. Unsupervised Learning to understand the two main types of machine learning.

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