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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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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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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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Technical Challenges of Model Deployment

Open Data Group July 25, 2018
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Deploying analytic models can be a long, slow moving process with many obstacles along the way. Many models are abandoned before they ever make it into production because of inefficiencies that slow down or halt the entire process. To overcome the challenges of model deployment, we need to identify the problems and learn what causes them. Some of the top technical challenges organizations face when trying to deploy a model into production are:

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ASA Datafest

Garrett Long March 14, 2017
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ODG is happy to announce our sponsorship and participation in two ASA Datafest events this spring. ODG will work with the Ohio State University in Columbus, OH and Loyola University in Chicago, IL to help students get the most from their weekends.  Our relationship with Datafest started last year, where we both mentored and judged the 2016 event at Loyola University.  It was a blast, and we were happy to see students really digging into the problem and finding unique solutions.

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Welcome Michael Barton - Director of Sales

Garrett Long November 29, 2016
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We are very excited to announce that Michael Barton joined Open Data Group today as Director of Sales.  With any small company, hiring sales is an important step - it must be timed right with both product market readiness.  Given the last 2 quarters of work on our product, and the reception of that work in the market, it's clear we needed help. 

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FastScore v1.2 - We live to deploy your data science models

Garrett Long November 10, 2016
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It's been a little while since we had a blog post, as we've been heads down on software development.  We've been working on improving FastScore with our partners and customers, as well as the input we are receiving from the market.  It's really exciting to announce FastScore v1.2 today, which marks the 3rd release in 6 months of the product.  The feature velocity is gaining steam, as are the demos.  

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AnalyticOps: Part 3 - What is an Analytic Engine?

Garrett Long May 25, 2016
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How are Analytic Engines different than “deployment servers”?

Well, did you do last week's homework? If you used the emerging analytic standards of PFA or PPFA to create an analytic for trajectory planning, please send it to info@opendatagroup.com. You just might get a job offer! Heck, if you use the open source tools Aurelius or Titus to convert your R or Python models to PFA we’d love to hear it, and share your work with the growing community of analytic engine users.

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