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The Year of Model Ops Series: A Customer Case Study

Garrett Long March 12, 2019
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As a company focused on the relatively new space of Model Operations, part of the challenge we face is getting external public validation of the value we are bringing to the market.  Many factors help in this regard including growing analyst coverage like those from Gartner, Forrester and 451 Research, partnerships (like ours with TIBCO), and most importantly business growth.  In particular, it’s often a seminal moment when a customer stands up on a stage and talks about your product and solutions publicly.  And, I am proud to share that one such moment has come for us here at Open Data Group with one of our key customers, Exos Financial.

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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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The Year of Model Ops Series: Thoughtful Expansion into the Market

Garrett Long February 12, 2019
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As we get further into 2019, “The Year of Model Operations”  I am happy to share two thoughtful moves further into our market.  One is centered on our participation in an upcoming conference, and the other shares our moves with an outstanding partner.

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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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Enabling a Machine Learning Transformation Within Your Organization

Open Data Group January 29, 2019
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Kicking Off 2019: The Year of Model Operations

Garrett Long January 15, 2019
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Welcome to 2019 – a year we here at ODG are calling “The Year of Model Operations”.  Throughout the year, we will be bringing a wide array “how to’s”,  customer and partner case studies and use cases – examples of what it means to execute machine learning strategies into production.  It’s an exciting time, as we’ve seen the shift of the market from “what is model operations” to “how do I do model 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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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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