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Things to Consider When Integrating Machine Learning into Your Infrastructure

machine learning infrastructureMachine 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.


Technical Questions & Considerations

While developing your machine learning strategy, here are a few technical considerations to think about:

  • How do we ensure that our machine learning capability supports the kinds of models and usage scenarios that the machine learning team will produce?

Your organization’s machine learning infrastructure should natively support all machine learning languages, packages, and tools. It should also support real-time streaming analytics and allow model deployment to be as efficient as possible.

  • Do non-ML experts require human supervision when they interact with the system?

The growth of self-service in data science means that a typical business user will execute models and retrieve results without expert intervention or oversight. Your organization should accommodate data scientists, as well as citizen data scientists, who are new to machine learning deployment.

  • Does the machine learning capability meet the scale, performance, and infrastructure requirements of production software components of the business?

Your machine learning capability won’t be effective unless they are meeting your organization’s specific needs. Depending on the size of your organization and amount of data you have, your machine learning infrastructure needs will vary. The infrastructure should be capable of running as many models as your organization needs and have the capability to grow with increases in demand.


Organizational Questions & Considerations

Sometimes organizational challenges can create even larger barriers when implementing a new system or adding to your current one. Organizational challenges of incorporating machine learning into your infrastructure include: 

  • Do we build our own machine learning capability, buy one, or a mixture of both?

Building your own machine learning capability probably seems like the most cost-efficient answer to this question, while buying seems like the quickest. While these options look like they will save you the most time or money, it is very unlikely that either of these options individually would be able to satisfy your organizations specific needs. A combination of build and buy is a good third option for organizations to utilize the resources they already have while incorporating the missing parts.

  • How do I include machine learning and analytics into the standard processes of our business?

The addition of machine learning technology to your organization doesn’t just affect one person or one team. Its effects drive deep into the organization’s structure and have big impacts on performance. Deciding what new systems need to be put in place, what training to provide employees, and what regulations to implement takes time and effort to make sure you get it right.

  • How do we ensure future change is built into the system?

Although implementing machine learning is a big change in itself, it’s safe to assume that won’t be the last change your organization ever makes. That’s why it’s important that your infrastructure allows for future changes without disrupting the entire system.


At Open Data Group, we realize that there are many considerations that must go into incorporating a machine learning technology into your infrastructure. To make this process a little easier, we help our customers integrate machine learning in a way that works for them using our machine learning deployment technology, FastScore. FastScore easily fits in to companies’ existing systems and can deploy machine learning models that scale, in any language.  FastScore is also very flexible, and is able to meet the organization’s needs, even as they change.

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