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The importance of customization


During the early days of NLP Logix, our co-founder and CIO, Matt Berseth, entered several data science and machine learning competitions.  He did well in all of them, and even won a few, but quickly learned that these particular AI/ML solutions were designed to win a competition, not designed to be used in the real world.


One of the most challenging parts of building an AI/ML solution isn’t necessarily building the machine learning model itself but getting the model into production to be used by an actual organization.  Every organization is different.  Different industries, systems, workflows, volumes – all these things vary from one company to another.  With so many variables to navigate, how do we get our models to run successfully in different environments?


Over the years, NLP Logix has built custom bindings which allows different programming languages to talk to each other.  These bindings act as our “tool kit” which allows a solution built in Python to seamlessly talk to system in another language such as Java or .NET.  This enables us to be system agnostic so we can work with clients with different IT systems. 


Our goal is to have as little disruption as possible to your IT team and systems.  The level of support and effort to deploy an AI/ML solution can also differ based on your organization.  For clients who may already have a capable technology team, we can create an API that the team can connect to in order to feed data into the model.  For clients who may not have an in-house team, we have software engineers that can help integrate the data and build out any connectivity as necessary. 


Putting An AI ML Solution Into Production


Testing is also critical when putting a ML model into production.  Not only are we testing the model for accuracy, but we are also testing the connectivity, and maybe most importantly, the capacity, in order to make sure the model can handle high volumes and is able to scale.  This is often a critical misstep which can lead to performance issues and downtime.


Over the last 9 years, NLP Logix has deployed hundreds of ML models and have learned (sometimes the hard way) where all the pitfalls are when working with data. 





Interested in hearing more about Automation?


Our team here at NLP Logix has been working on these types of projects for over 8 years and can execute automation assessments quickly and efficiently.  If your organization is interested in exploring AI/ML projects but not sure where to start, feel free to contact us at 904.208.5065



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