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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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Byline: Monetizing your models with machine learning solutions

Open Data Group April 26, 2018
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Article: Monetizing your models with machine learning solutions, by Stu Bailey, Contributor, InfoWorld

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Key steps to model creation: data cleaning and data exploration

Open Data Group March 1, 2018
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Article: Key Steps to Model Creation: Data Cleaning & Data Exploration, by Stu Bailey, Contributor, InfoWorld

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Where are you in your analytic journey?

Open Data Group December 18, 2017
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Article: The secrets to successfully accomplishing each step in your analytic journey

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Modern IT and Data Science in an Era of Analytic Deployment

Open Data Group November 30, 2017
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Transitioning from a monolithic platform to a modular approach when building models

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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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Welcome Rehgan Avon to Open Data Group

Ginger Phelps July 17, 2017
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RehganAvon.pngWe are excited to introduce Rehgan Avon as the newest addition to the Open Data Group (ODG) team.  Rehgan comes on board as a Product Manager, with a background in integrated systems engineering and a strong focus on analytical technology.  She has easily made the transition from data and systems engineering to product strategy.

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How To Score Big Data At Scale

Ginger Phelps July 12, 2017
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Modern businesses leverage analytics to gain insights in a multitude of areas, from evaluating business performance to predicting future behaviors. In many industries, these insights are quantified numerically as “scores,” and the process of applying an analytic model to transform a collection of data into scores is called “scoring.”

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Ideas Through Creating Value: How do we get there?

Ginger Phelps July 6, 2017
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Imagine that you created a model that runs without any errors. There were no miscalculations the first time Data Science tested it, IT could easily read and replicate it, and it was deployed within a few hours of being trained and approved. In a perfect world, this process may be a bit more realistic. However, this ease of creating a well-performing model is not always seen.  

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