Machine Learning Workflow
End-to-End View

Machine Learning Workflow

A practical, end‑to‑end view of how real Machine Learning projects move from raw data to deployed models and continuous improvement.

High-level Stages

1. Problem Framing — what business question are we answering? What is the prediction target?
2. Data Collection & Understanding — gather data, explore distributions, spot leaks and biases.
3. Data Preprocessing & Feature Engineering — clean, transform and create features suitable for modeling.
4. Model Training & Selection — try different algorithms and tune hyperparameters.
5. Evaluation & Validation — measure generalization performance using proper metrics and CV.
6. Deployment & Monitoring — serve predictions in production and monitor for drift and degradation.

1. Problem Framing

Good ML starts with a clear problem statement. Examples:

  • “Predict probability of churn in the next 30 days.”
  • “Forecast demand for each product next week.”

2. Data Collection & Understanding

Identify data sources (databases, logs, APIs), then use EDA (exploratory data analysis) to understand quality and patterns.

3. Data Preprocessing & Feature Engineering

This stage connects to the dedicated Data Preprocessing page and typically includes:

  • Handling missing values and outliers.
  • Encoding categorical variables.
  • Scaling and normalizing numeric features.
  • Creating domain‑specific features.

4. Model Training & Selection

We choose algorithms based on problem type, data size and constraints, then tune hyperparameters using validation data or cross‑validation.

5. Deployment & Monitoring

Models only create value when they are integrated into products or decision processes:

  • Expose models through APIs or batch jobs.
  • Monitor latency, error rates and prediction quality.
  • Detect data drift and retrain when needed.
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