Machine Learning Workflow

Machine Learning is not just about choosing an algorithm and training a model. In real-world projects, ML follows a structured workflow that ensures models are accurate, reliable, and usable in production.

Understanding the Machine Learning workflow helps you build systems that scale beyond experiments and perform well in real applications.

What Is an ML Workflow?

A Machine Learning workflow is the end-to-end process used to design, train, evaluate, deploy, and maintain ML models.

Each step in the workflow plays a critical role. Skipping or rushing steps often leads to poor model performance.

Real-World Connection

Consider a system that predicts house prices.

• Data is collected from past house sales
• Data is cleaned and prepared
• A model learns patterns from the data
• The model is tested on unseen houses
• The system is deployed for real users

This process represents a complete Machine Learning workflow.

Core Stages of the ML Workflow

Most Machine Learning projects follow these stages:

• Problem definition
• Data collection
• Data preprocessing
• Feature engineering
• Model training
• Model evaluation
• Deployment
• Monitoring and improvement

Stage 1: Problem Definition

Before writing any code, the problem must be clearly defined.

This step answers questions such as:

• What is being predicted?
• What data is available?
• How will success be measured?

A well-defined problem leads to better model design.

Stage 2: Data Collection

Data is collected from sources such as databases, APIs, sensors, or user interactions.

The quality and relevance of collected data directly affect model performance.

Stage 3: Data Preprocessing

Raw data is cleaned and transformed into a usable format.

• Handling missing values
• Removing noise and duplicates
• Normalizing or scaling features

This step prepares data for effective learning.

Stage 4: Feature Engineering

Features are the inputs used by ML models.

Good features highlight meaningful patterns, while poor features hide them.

Feature engineering often determines whether a model succeeds or fails.

Stage 5: Model Training

During training, the model learns patterns from the training data.

The learning algorithm adjusts model parameters to minimize prediction errors.

Stage 6: Model Evaluation

After training, the model is tested using unseen data.

Evaluation ensures the model generalizes well and does not simply memorize the training data.

Stage 7: Deployment

Deployment makes the trained model available for real users.

Models can be deployed as APIs, embedded in applications, or integrated into cloud services.

Stage 8: Monitoring and Improvement

Once deployed, models must be monitored continuously.

• Detect performance drops
• Handle new data patterns
• Retrain models when necessary

Machine Learning systems improve over time through continuous feedback.

Simple ML Workflow Example

The example below shows a simplified representation of an ML workflow.

def ml_workflow():
    define_problem()
    collect_data()
    preprocess_data()
    train_model()
    evaluate_model()
    deploy_model()
    monitor_model()
  

This illustrates the logical flow followed in real Machine Learning projects.

Practice Questions

Quick Quiz

Coming up next: Supervised Learning — how machines learn from labeled examples.