Modeling Predictive Modeling Testing 
Analytics Vidhya
FEBRUARY 25, 2023
Introduction Often while working on predictive modeling, it is a common observation that most of the time model has good accuracy for the training data and lesser accuracy for the test data.
KDnuggets
AUGUST 31, 2022
When building a predictive model, the quality of the results depends on the data you use. In order to do so, you need to understand the difference between training and testing data in machine learning.
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DataRobot Blog
JULY 22, 2022
Using AI-based models increases your organization’s revenue, improves operational efficiency, and enhances client relationships. You need to know where your deployed models are, what they do, the data they use, the results they produce, and who relies upon their results. That requires a good model governance framework.
DataKitchen
SEPTEMBER 21, 2023
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. Running these automated tests as part of your DataOps and Data Observability strategy allows for early detection of discrepancies or errors.
Smarten
SEPTEMBER 30, 2024
Knowledgebase Articles Datasets & Cubes : Spark Sql : Split the dataset columns using Regex Dashboards Improve dashboard loading performance for dashboards with multiple objects Integration API : API Call to rebuild cubes / datasets Predictive Use cases Assisted predictive modelling : Classification : Machine Maintenance Using Smarten Assisted (..)
O'Reilly on Data
DECEMBER 12, 2019
Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]
O'Reilly on Data
JANUARY 17, 2023
Building Models. A common task for a data scientist is to build a predictive model. You’ll try this with a few other algorithms, and their respective tuning parameters–maybe even break out TensorFlow to build a custom neural net along the way–and the winning model will be the one that heads to production.
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