Track Experiments
Track your model training experiments by recording hyperparameters and metrics like loss and accuracy
Model training is an extensive process where you go through cycles of tuning hyperparameters and understanding how changes impact the convergence of your models, accuracy against the validation set, system resource utilization, etc. Understanding how a model trains against a set of hyperparameters is crucial for optimizing experimentation results.
MarkovML facilitates this process by allowing you to record your model training Experiments using the Python SDK. You can then inspect metrics like loss and accuracy and system metrics like CPU and memory usage against the set of hyperparameters provided.
The following sections describe how you can track your experiments with MarkovML.
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