A step-by-step information to monitoring mannequin efficiency with real-time metrics and visualizations.
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Learn to construct a customized ML monitoring dashboard in Python with Plotly, Sprint, and FastAPI to trace metrics in real-time.
Coaching an ML mannequin is only the start. The actual problem begins as soon as the mannequin goes stay. Is it nonetheless correct? Are predictions drifting? With out monitoring, you’re flying blind.
I lately constructed a customized ML monitoring dashboard in Python to regulate my fashions in manufacturing. Right here’s how I did it — and how one can too.
Machine studying methods degrade over time. Some frequent points:
Information drift: The incoming knowledge seems to be completely different than the coaching knowledge.
Idea drift: Relationships between options and targets change.
Efficiency drops: Accuracy, F1, or recall decline silently.
With out monitoring, you received’t spot these till it’s too late.
To maintain issues light-weight and Python-native, I picked:
