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Organizational Impact of Machine Learning Transformation

Open Data Group February 27, 2019
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             In a previous blog, we discussed how companies can enable a machine learning transformation within their organization. One key element for successful transformation is the organizational alignment to this goal. Leadership must ensure that each employee and department is aligned toward the goal of enabling machine learning within the organization. In addition, clear and demonstrable accountability is paramount.   It’s not enough that everyone in the organization is aware of the goals and objectives of machine learning, but they should also know the role that they play in it.

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Introducing FastScore 1.9: Enabling the Transformation of Model Operations

Reagan Avon February 8, 2019
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As we move into 2019, Open Data Group is seeing a very powerful secular shift towards the implementation of machine learning models, a trend recently mentioned in our blog “Kicking Off 2019: The Year of Model Operations”.  In connection with these trends, Open Data Group is pleased to announce the release of FastScore 1.9, which continues to grow our model operations offering and capabilities. As a Docker based microservice approach, FastScore provides enhanced functionality to address the emerging needs of machine learning operations.

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Differences in the Creation and Production Environments

Open Data Group December 11, 2018
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 When it comes to machine learning models, there are many differences between creation environments (where the model was built), and production environments (where the model will be used, monitored and have it's life cycle managed). The creation environment is oriented towards a specific set of people working on the model, with specific system, data and outputs configurations. But the production environment may be quite different - with other people, systems and requirements applied to the model.  Understanding these differences allows organizations to be efficient in both environments, and know how to best navigate for the full life cycle of their critical machine learning assets. Let’s take a deeper look at the differences between creation and production environment in order to increase the effectiveness of our deployment process.

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