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How Your Approach to Model Management & Deployment Can Add Value to Your Business

Open Data Group August 7, 2018

model management value blogMachine 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.

 

Run Multiple Models Effectively

The primary purpose of implementing a model management strategy in the deployment process is its ability to maintain a number of models at once. Proper model management prevents key parts of the deployment process from slowing down while searching for assets, especially when dealing with more than one organization or model in production. Model management provides a central repository system for all of the models and related assets.  Importantly, the model management system should, as much as possible, mirror existing processes and capabilities for tracking code assets in an enterprise.  In doing so, the model management system has the greatest chance to be fully leveraged and adopted across the organization. 

 

Manage Metadata and User Information

Another benefit of model management is the ability to track metadata and historical user information. Many companies have a requirement to track model lineage for auditing purposes.  A well-designed model management capability  creates a more accessible model lineage by tracking and organizing all model related data in a user friendly, easy to use system where data can be clearly found. This feature provides transparency and saves time when searching for historical pieces of data and makes the overall process of analytic deployment more efficient.

 

Streamlined Deployment

Model management provides increased efficiency to the deployment process. Incorporating a management system into your business processes helps delineate clear roles and responsibilities, process check points and hand-offs, while simultaneously decreasing the amount of time spent on tracking audits and understanding changes made to your models. Tracking models by hand takes much more time and resources than a good model management system, and will eventually lead to wasted time and resources.

 

Improve Team Collaboration

Finally, model management tools help your team collaborate more effectively by creating a centralized model repository. This allows users to review and leverage an existing catalog of models and related assets, and leverage them for new projects. This added flexibility is an important aspect of efficiently scaling out machine learning adoption.

 

Incorporating a model management system has many advantages that speed up the analytic deployment process. With a good approach to model management, organizations are able to save time and money, collaborate more effectively with team members, and track their models much more efficiently. This increased efficiency allows companies to derive value from their models that much quicker. Because of these added benefits, one of the first things we built for our product here at Open Data Group was a model management capability. We believe that in order to have a good foundation when deploying your analytic model, there must be a solid, integrated system to track and organize all aspects of the model.

 

To learn more about how we incorporate model management into our deployment engine, FastScore, check out this product video of our model management tool. Stay up to date with Open Data Group by visiting our website or registering for a webinar!

 

Topics: Machine Learning, Model Management, Model Manage, Open Data Group, analytics, Analytic Deployment