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Discuss with stakeholders how accuracy and datadrift will be monitored. Typical data quality checks and corrections include: Missing data or incomplete records Inconsistent data formatting (e.g., mixture of dollars and euros in a currency field) Inconsistent coding of categoricaldata (e.g.,
The second is drift. Then there’s data quality, and then explainability. That falls into three categories of model drift, which are prediction drift, datadrift, and concept drift. Approaching drift resolution looks very similar to how we approach performance tracing.
The second is drift. Then there’s data quality, and then explainability. That falls into three categories of model drift, which are prediction drift, datadrift, and concept drift. Approaching drift resolution looks very similar to how we approach performance tracing.
The second is drift. Then there’s data quality, and then explainability. That falls into three categories of model drift, which are prediction drift, datadrift, and concept drift. Approaching drift resolution looks very similar to how we approach performance tracing.
This explainability of the predictions can help you see how and why the AI came to these predictions. Set up a data pipeline that delivers predictions to HubSpot and automatically initiate offers within the business rules you set. A look at datadrift. A clear picture of the model’s accuracy.
Together, these data ops efforts ensure that model development time is efficient, model performance is robust, and teams focus more on innovation and customer experience, which is what matters. The piece that connects the model to the application and the data is the explainability of the model. Bayan Bruss: Thanks Kishore.
Together, these data ops efforts ensure that model development time is efficient, model performance is robust, and teams focus more on innovation and customer experience, which is what matters. The piece that connects the model to the application and the data is the explainability of the model. Bayan Bruss: Thanks Kishore.
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