ML Pipeline Process

What is an ML Pipeline?

An ML Pipeline (Machine Learning Pipeline) is an automated workflow that combines data preprocessing, feature engineering, model training, and evaluation into a streamlined process.

Pipeline Steps in CMMI-DCC

1. Data Selection: Choose data types and features
2. Preprocessing: Handle missing values, scale features
3. Feature Selection: Identify most important variables
4. Model Training: Train selected algorithm
5. Evaluation: Assess performance using cross-validation
6. Results: Feature importance, metrics, predictions

Creating a Pipeline

Navigate to Analysis -> ML Pipelines -> New ML Pipeline

Pipeline Status

• Queued: Waiting to start
• Processing: Currently running
• Completed: Finished successfully
• Failed: Encountered an error

Related Terms

• Cross-Validation: Model evaluation technique
• Feature Selection: Choosing important variables
• Hyperparameter Tuning: Optimizing model parameters