The transcript discusses the development life cycle of machine learning models without following MLOps principles.
It emphasizes the importance of continuously updating and retraining models as new data and business changes occur.
MLOps principles include continuous integration and continuous development, with a focus on a single team working together rather than separate entities.
The use of pipelines is highlighted for modularizing and generalizing code to ensure reusability.
The final model developed based on the latest data is deployed and used for prediction, with an emphasis on storing and saving all the data in a data store.

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