Machine Learning Workflow
Learn the steps involved in a typical machine learning workflow, from problem definition to model deployment. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples
45 min•By Priygop Team•Last updated: Feb 2026
Problem Definition
- Clearly define the business problem — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Identify the type of ML problem (classification, regression, etc.)
- Set success metrics and evaluation criteria — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Determine data requirements — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Data Collection
- Gather relevant data from various sources — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Ensure data quality and completeness — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Handle missing values and outliers — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Document data sources and collection methods — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
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 — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Tune hyperparameters using validation data — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Evaluate model performance — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Model Evaluation
- Use appropriate metrics (accuracy, precision, recall, F1-score)
- Cross-validation for robust evaluation — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Compare multiple models — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Analyze model errors and biases — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
Model Deployment
- Deploy model to production environment — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Monitor model performance — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Implement model updates and retraining — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects
- Ensure scalability and reliability — a critical concept in artificial intelligence and machine learning that you will use frequently in real projects