Machine Learning Workflow
Learn the steps involved in a typical machine learning workflow, from problem definition to model deployment.
45 min•By Priygop Team•Last updated: Feb 2026
Problem Definition
- Clearly define the business problem
- Identify the type of ML problem (classification, regression, etc.)
- Set success metrics and evaluation criteria
- Determine data requirements
Data Collection
- Gather relevant data from various sources
- Ensure data quality and completeness
- Handle missing values and outliers
- Document data sources and collection methods
Data Preprocessing
- Data Cleaning: Remove duplicates, handle missing values
- Feature Engineering: Create new features from existing data
- Data Transformation: Normalize, scale, encode categorical variables
- Data Splitting: Train/validation/test sets
Model Selection & Training
- Choose appropriate algorithms based on problem type
- Train models on training data
- Tune hyperparameters using validation data
- Evaluate model performance
Model Evaluation
- Use appropriate metrics (accuracy, precision, recall, F1-score)
- Cross-validation for robust evaluation
- Compare multiple models
- Analyze model errors and biases
Model Deployment
- Deploy model to production environment
- Monitor model performance
- Implement model updates and retraining
- Ensure scalability and reliability