课程: MLOps Essentials: Model Development and Integration

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Experiment tracking

Experiment tracking

课程: MLOps Essentials: Model Development and Integration

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Experiment tracking

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- [Instructor] One of the critical areas for MLOps is the tracking of ML experiments. In ML training, multiple runs of building and validating the model happens as the data scientists work towards their expected performance goals. Each ML training run is considered an experiment. Experiment tracking helps manage the evolution of an ML model towards stated performance goals. Experiments should be tracked continuously to analyze if improvements are made and decide on the next set of experiments to run. What should be tracked for an experiment? We begin with the model itself. All the model set up including the ML algorithm being used and the architecture of the model for deep learning models need to be tracked. Also the hyper parameters set up for the specific experiment should be tracked. Next comes the input to modeling. The specific data set and its version should be linked to the model. The training validation and test…

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