Machine Learning Workflow: From Data to Deployment

A complete guide to the machine learning workflow, covering data preparation, model training, evaluation, and deployment.

The ML Workflow

Every machine learning project follows a systematic workflow. Understanding this process is crucial for success.

1. Problem Definition

Before writing any code, clearly define:

What problem are you solving?
What is the target variable?
What metrics will define success?

2. Data Collection & Preparation

import pandas as pd
from sklearn.model_selection import train_test_split
 
# Load data
df = pd.read_csv('dataset.csv')
 
# Split features and target
X = df.drop('target', axis=1)
y = df['target']
 
# Train/test split
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

3. Feature Engineering

from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
 
# Define preprocessing
numeric_features = ['age', 'income']
categorical_features = ['category', 'region']
 
preprocessor = ColumnTransformer([
    ('num', StandardScaler(), numeric_features),
    ('cat', OneHotEncoder(), categorical_features)
])

4. Model Training

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
 
# Create pipeline
pipeline = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', RandomForestClassifier(n_estimators=100))
])
 
# Train
pipeline.fit(X_train, y_train)

5. Model Evaluation

from sklearn.metrics import classification_report, confusion_matrix
 
# Predictions
y_pred = pipeline.predict(X_test)
 
# Evaluation
print(classification_report(y_test, y_pred))
print(confusion_matrix(y_test, y_pred))

6. Hyperparameter Tuning

from sklearn.model_selection import GridSearchCV
 
param_grid = {
    'classifier__n_estimators': [50, 100, 200],
    'classifier__max_depth': [5, 10, None]
}
 
grid_search = GridSearchCV(pipeline, param_grid, cv=5)
grid_search.fit(X_train, y_train)
 
print(f"Best params: {grid_search.best_params_}")

7. Model Deployment

import joblib
 
# Save model
joblib.dump(pipeline, 'model.pkl')
 
# Load model
loaded_model = joblib.load('model.pkl')
predictions = loaded_model.predict(new_data)

Key Takeaways

Iterate - ML is iterative, not linear
Validate - Always use cross-validation
Document - Track experiments and results
Monitor - Models degrade over time

Happy Building! 🚀