Blog

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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Introducing FastScore 1.8 Functionality

Brendan Kelly September 13, 2018
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Open Data Group is excited to announce the release of our latest version, 1.8. This release includes functionality to address the new emerging needs we have identified in the market and feedback from our customers.  The new features in this release expand the functionality of the FastScore solution to meet the needs of an enterprise system at scale.

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Things to Consider When Integrating Machine Learning into Your Infrastructure

Open Data Group September 4, 2018
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Machine learning has changed the way we leverage and apply analytic models, and it isn’t going away anytime soon. As more and more organizations bring machine learning into their analytic portfolio, benefits are becoming clearer. Machine learning increases efficiencies in many applications once it’s integrated into an organization’s infrastructure, but getting to that point comes with many challenges. Some challenges of incorporating machine learning into your company’s infrastructure can be technical, while others are strategic.

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Benefits of Implementing a Microservices Based Architecture

Open Data Group August 21, 2018
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Today data scientists use more tools than ever when creating and deploying analytic models. According to Rexer Analytics, the typical data scientist uses an average of 5 tools in their daily job. This variety of tools can provide difficulties for IT later on when trying to deploy the model, especially if the organization uses a monolithic platform that locks the company into a set coding language. Today’s data science tooling was not built for large scale deployment enterprise systems, which is why many organizations are switching to a microservices based architecture.

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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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Designing Your Analytic Development Life Cycle

Open Data Group July 12, 2018
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It has become clear that analytics are critical intellectual property for the enterprise with the potential to have huge business impacts.  However, the value companies derive from their models may be hindered by lack of a strong analytic development life cycle(ADLC) process.  Without a well conceived analytic development life cycle, models in production may not work to entitlement, and the rate of iteration may be hindered. It is essential to design your analytic development life cycle to create a scalable and efficient downstream pipeline that moves models efficiently from creation to production. 

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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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