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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

The uncertainty of not knowing where data issues will crop up next and the tiresome game of ‘who’s to blame’ when pinpointing the failure. In the context of Data in Place, validating data quality automatically with Business Domain Tests is imperative for ensuring the trustworthiness of your data assets.

Testing 169
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How to Set AI Goals

O'Reilly on Data

Technical competence results in reduced risk and uncertainty. With well-formed goals, data scientists and machine learning engineers can then apply the scientific method to test different approaches in order to determine the validity of the hypothesis, and assess whether a given approach is feasible and can achieve the goal.

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Humans-in-the-loop forecasting: integrating data science and business planning

The Unofficial Google Data Science Blog

This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. A single model may also not shed light on the uncertainty range we actually face. For example, we may prefer one model to generate a range, but use a second scenario-based model to “stress test” the range.

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Predicting Movie Profitability and Risk at the Pre-production Phase

Insight

I held out 20% of this as a test set and used the remainder for training and validation. Feature Selection and Engineering Most of the inputs to my model were taken either as is from the data source, or with minimal processing. Scatterplot of the predicted ROI vs. the true ROI for the hold-out test set. A New Hope ).

Risk 67
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Using random effects models in prediction problems

The Unofficial Google Data Science Blog

In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution. These predictive posterior distributions have many uses such as in multi-armed bandit problems. bandit problems). ICML, (2005). [3]