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- Cesar Velasquez
Advancements in machine learning technology have greatly streamlined the ability to create predictive models. However, building a reliable predictive model still requires the successful completion of multiple stages, from scanning input data to validating the results of the model. This lab will guide you through the IHS Markit methodology of building predictive models to provide additional insights on how to increase asset performance.
Conditioning the data is the most time consuming, yet critical part of building ML models. In this lab we will discuss model conditioning and the importance of repeatable analytics ready data models. Attendees will then have the ability to work with and create predictive production models to plan and optimize new wells.