Machine Learning Workflow

Machine Learning is not just about applying an algorithm. It follows a clear and logical workflow that transforms raw data into a trained and reliable model.

If any step in this workflow is ignored or done incorrectly, the final model performance will suffer.

What is a Machine Learning Workflow?

A Machine Learning workflow is a structured sequence of steps used to build, evaluate, and deploy ML models.

Think of it as a roadmap that guides us from raw data → trained model → predictions.

High-Level Steps in ML Workflow

• Problem Definition
• Data Collection
• Data Preprocessing
• Feature Engineering
• Model Selection
• Model Training
• Model Evaluation
• Model Deployment

Let us go through each step clearly.

1. Problem Definition

This is the most important step. Here, we clearly define what problem we want to solve.

• Are we predicting a value or a category?
• Is it a business or technical problem?
• What does success look like?

Example: Predicting house prices based on size, location, and number of rooms.

2. Data Collection

Machine Learning depends heavily on data quality. In this step, we gather relevant data from various sources.

• Databases
• CSV or Excel files
• APIs
• Web scraping

More data is useful, but only if it is relevant and accurate.

3. Data Preprocessing

Raw data is rarely clean. This step prepares data for modeling.

• Handling missing values
• Removing duplicates
• Fixing incorrect data
• Converting data types

Poor preprocessing leads to unreliable models.

4. Feature Engineering

Features are input variables used by the model. Feature engineering improves model performance.

• Selecting important features
• Creating new features
• Removing irrelevant features

Good features often matter more than complex algorithms.

5. Model Selection

Different problems require different algorithms.

• Linear Regression for numeric prediction
• Logistic Regression for classification
• Decision Trees for rule-based learning

The goal is to choose a model suitable for the data and problem.

6. Model Training (With Code Example)

During training, the model learns patterns from data.

from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split

X = [[1], [2], [3], [4], [5]]
y = [2, 4, 6, 8, 10]

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

model = LinearRegression()
model.fit(X_train, y_train)

Code Explanation

• train_test_split: splits data into training and testing sets
• model.fit(): trains the model on training data
• The model learns the relationship between X and y

7. Model Evaluation

Evaluation tells us how well the model performs on unseen data.

• Accuracy
• Precision
• Recall
• Mean Squared Error

Never evaluate on training data only.

8. Model Deployment

Deployment means making the model available for real use.

• Web applications
• Mobile apps
• APIs

After deployment, models must be monitored and updated.

Real-World Workflow Example

Spam Email Detection:

• Define problem: spam or not spam
• Collect email data
• Clean text data
• Extract features
• Train classification model
• Evaluate accuracy
• Deploy to email system

Mini Practice

For a movie recommendation system:

• What is the problem definition?
• What data would you collect?
• What type of model would you choose?

Exercises

Exercise 1: List all steps of the ML workflow in correct order.

Exercise 2: Why is data preprocessing important?

Exercise 3: What happens if we skip model evaluation?

Exercise Answers

• Answer 1: Problem → Data → Preprocessing → Features → Model → Training → Evaluation → Deployment
• Answer 2: Because raw data contains errors and noise
• Answer 3: We cannot trust model predictions

Quick Quiz

In the next lesson, we will learn how to prepare data properly using data preprocessing techniques.