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ODG and BlueData - Accelerating Model Operations

Open Data Group August 8, 2019
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Open Data Group is pleased to announce that we now support our flagship FastScore™AI/ML model operations platform on BlueData’s EPIC™ enterprise AI/ML/BigData solution. We are excited to be part of the BlueData eco-system and plan to support BlueData in all of our future releases. The integration enables large enterprises to more rapidly convert insights from data scientists into realized value by slashing the time required to launch and track analytic models in production.

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Open Data Group Announces FastScore™ Release 1.10

Open Data Group June 11, 2019
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New Features for Leading Model Operations System Further Automate and Accelerate Deployment of Analytic Models into Production

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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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Enabling a Machine Learning Transformation Within Your Organization

Open Data Group January 29, 2019
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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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Model Assessment and Model Traceability

Open Data Group November 27, 2018
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Model assessment and model traceability are two crucial steps when deploying a machine learning model. Model assessment ensures that the model is running accurately and efficiently, while model traceability deals with the history of the machine learning model. These two components are critical for deployment, but how can we make sure we are successful in these areas? Today we are going to dive into model assessment and model traceability, and discuss the importance of these processes.

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Benefits of the Cloud: IT vs. Data Science

Open Data Group October 30, 2018
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Deploying machine learning models is often a bottle neck in realizing the value from data science investments. Utilizing the cloud, combined with a microservices based infrastructure, to deploy machine learning models can make the process less complex, and make life easier for everyone involved. Let’s look into how analytic migration to the cloud can help the data science and IT teams specifically:

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Combining Cloud and Microservices to Deploy Machine Learning Models

Open Data Group October 16, 2018
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In one of our past blogs, we discussed the importance of the cloud and its many benefits, including better security, more storage, increased collaboration, cost effectiveness, and redundancy. Because the cloud has been a topic of much discussion in the past few years, most people understand these benefits of utilizing the cloud in their organization. Now we want to dive in a little deeper to determine why the cloud is so important specifically for deploying machine learning models and analytics.

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How to Monitor Your Machine Learning Models

Open Data Group October 2, 2018
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In our previous blog we discussed how to deploy machine learning models into production successfully and efficiently. Getting models into production can be difficult, but that isn’t the only challenge you will face with machine learning models throughout their lifetime. Once the model has made it into production, it must be monitored in order to ensure that everything is working properly. There are many different roles involved in the getting the model into production. Similarly, the monitoring of each machine learning model requires the attention from many different perspectives to ensure that each aspect of the model is running accurately and efficiently.  Let’s take a closer look into the different perspectives we must consider when monitoring machine learning models, and why each is so important:

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Increase Efficiency Between Data Science & IT When Deploying Models into Production

Open Data Group September 18, 2018
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Deploying analytic models into production can often prove to be a difficult and tedious process. In an ideal world, data scientists create a model, they hand it off to IT, and IT puts that model into the production environment. Seems simple enough, right? However, as many data science and IT teams know, there are many complications that can turn this process from a simple one, to a highly complex back and forth.

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