What is it? A machine learning pipeline is a set of steps that are used to build and deploy a machine learning model which usually includes 8 steps:
- Problem Definition: Define the business problem
- Data Ingestion: Identify and collect the dataset
- Data Preparation: Process and prepare the data
- Data Segregation: Split data into training/validation/testing set
- Model Training: Train the models against the training dataset
- Candidate Model Evaluation: Measure the performance of the models
- Model Deployment: Deploy into production
- Performance Monitoring: Monitor performance, retrain and calibrate
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