ML cube Platform

Kāpēc ML cube Platform?

In AI in production, a crucial challenge emerges: non-stationarity of data, which compromise the effectiveness and reliability of algorithms.
ML cube Platform addresses it by providing by proactively detecting changes in data and operating conditions that could degrade model accuracy and potentially cause production issues.
It helps AI developers to improve the reliability of their AI systems deployed on Siemens Industrial Edge.

Priekšrocības

ML cube Platform provides visibility into model performance, data, and behavior in production, enabling early detection of anomalies and ensuring transparency and reliability.
ML cube Platform speed up root cause analysis by linking performance drops to data, model, or system changes, reducing debugging time.
Enables proactive responses to data drift, such as retraining models or adjusting data pipelines, ensuring sustained accuracy and performance.

Piedāvātās iespējas

Active Monitoring

Continuously tracks model performance, inputs, image metrics, and outputs in real time, generating alerts for anomalies or degradation to ensure reliability and quick response.

Drift Explainability

Identifies and explains why drift occurs by highlighting which image features or data segments changed, helping teams quickly understand and address root causes.

Dynamic clustering

Groups data into evolving clusters based on patterns, enabling detection of shifts in data segments and uncovering hidden changes affecting model performance.

Reālās pasaules lietojumprogrammas

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Fruit Defect Identification

The ML cube Platform monitored the input images and correctly identified an unexpected seasonality drift, which was causing a model performance deterioration. The dynamic clustering and drift explainability modules allowed the user to understand the source of the drift.

ML cube Platform darbina ar ML cube