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Why is Git Not the Best for ML Model Version Control

The MLOps Blog

Further, maintaining model versions will save the risk of losing the model details in case the original model developer is longer working on the project. You also need to store model metadata and document details like configuration, flow, and intent of performing the experiments. Git cannot also automatically log each experiment.

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How to Build a CI/CD MLOps Pipeline [Case Study]

The MLOps Blog

Cost and resource requirements There are several cost-related constraints we had to consider when we ventured into the ML model deployment journey Data storage costs: Storing the data used to train and test the model, as well as any new data used for prediction, can add to the cost of deployment. S3 buckets.

ETL 52