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How Your Approach to Model Management & Deployment Can Add Value to Your Business

Open Data Group August 7, 2018
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Machine learning models have the ability to provide tremendous amounts of value to the companies that utilize them. Models that lead to business and market insights can be an important differentiator for organizations, and can end up being a strategic advantage for the entire business.  Although the value added can be significant, model deployment is a process with many moving parts, including tracking large volumes of machine learning models, managing data science language packages and assets, monitoring the models’ data for training and production, and tracking organizational issues like permissions on each model. This complexity leads to a new trend:  implementing a model management strategy. Model management systems are used to track each model and its assets. A well thought out approach to model management allows organizations to fully leverage their models and differentiate themselves from competitors. Let’s look into the specific ways that an approach to model management can help you keep your deployment process efficient and organized, while saving you valuable time.

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3 Ways Model Management Helps You Get Organized

Ginger Phelps June 20, 2017
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Imagine tracking data for multiple models by hand. How long would this process take you? Hours? Days? This question mainly depends on how many models you need to track and how much information there needs to be maintained within the models.

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