Simplifying the Machine Learning lifecycle Every machine learning model always has a model life cycle behind the scenes – it starts with data versioning, then data validation, often some pre-processing (to map real-world data to something that our model understands), model training, architecture, some tuning, validation for analysis, and finally deployment. If you’re a data […]
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