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.