Machine Learning Workflow
Machine Learning Workflow is the structured process of developing, deploying, and maintaining a machine learning system. It typically includes collecting and preparing data, training and evaluating models, deploying them, and continuously monitoring their performance.
What is Machine Learning Workflow?
A machine learning workflow typically begins with defining the problem and collecting relevant data. The data is then cleaned, prepared, and used to train a model. After evaluation and validation, the model can be deployed into production. Monitoring and maintenance help identify issues such as performance degradation, data drift, or changing requirements over time.
Why is Machine Learning Workflow Important?
A well-defined workflow helps teams develop machine learning systems consistently and efficiently. It provides structure across the model lifecycle, improves reproducibility, and makes it easier to identify and address problems before and after deployment.
Common use cases
Machine learning workflows are commonly used for predictive modeling, classification, recommendation systems, fraud detection, computer vision, natural language processing, and production AI applications.