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Technical Challenges of Model Deployment

Open Data Group July 25, 2018
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Deploying analytic models can be a long, slow moving process with many obstacles along the way. Many models are abandoned before they ever make it into production because of inefficiencies that slow down or halt the entire process. To overcome the challenges of model deployment, we need to identify the problems and learn what causes them. Some of the top technical challenges organizations face when trying to deploy a model into production are:

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Why Your Models Are Getting Lost in Translation

Ginger Phelps July 25, 2017
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Currently there are innumerable data languages that can be used for a wide range of analytic projects, and this amount will surely increase as new languages are being developed.

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The Evolution from PMML and PFA to Agnostic Scoring Engines

Ginger Phelps July 18, 2017
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Before models can be placed into scoring engines and then into production, custom code has to be written for each model. This process is labor-intensive and often error-prone. After the model’s custom code is written, Data Scientists have to transport the model to IT.  

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