A client was running several artificial intelligence models and needed to track their performance in real time. A metrics dashboard with time-series history.
AI models can degrade silently. A model that worked well at launch can lose accuracy over time if the input data changes (data drift). Without monitoring, this degradation goes unnoticed until the results become visibly poor.
The metrics platform continuously monitors the performance of each model: accuracy, response time, error rate and prediction volume. The time-series history makes it possible to detect trends over time.
The visual dashboards give data science teams a clear view without having to write manual queries.
Real-time tracking of AI model performance makes it possible to detect degradations before they affect end users.
The time-series metrics history makes it possible to track how performance evolves over time.
The visual dashboards give data science teams instant visibility without writing queries.
High-frequency metric processing ensures fine-grained tracking of performance in production.
Live AI metrics
React/Redux visualization
Trend tracking
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