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Article: Monetizing your models with machine learning solutions, by Stu Bailey, Contributor, InfoWorld

Article: Key Steps to Model Creation: Data Cleaning & Data Exploration, by Stu Bailey, Contributor, InfoWorld

Article: The secrets to successfully accomplishing each step in your analytic journey

Transitioning from a monolithic platform to a modular approach when building models

Currently there are innumerable data languages that can be used for a wide range of analytic projects, and this amount will surely increase as new languages are being developed.

Before models can be placed into scoring engines and then into production, custom code has to be written for each model. This process is labor-intensive and often error-prone. After the model’s custom code is written, Data Scientists have to transport the model to IT.  

Modern businesses leverage analytics to gain insights in a multitude of areas, from evaluating business performance to predicting future behaviors. In many industries, these insights are quantified numerically as “scores,” and the process of applying an analytic model to transform a collection of data into scores is called “scoring.”

Imagine that you created a model that runs without any errors. There were no miscalculations the first time Data Science tested it, IT could easily read and replicate it, and it was deployed within a few hours of being trained and approved. In a perfect world, this process may be a bit more realistic. However, this ease of creating a well-performing model is not always seen.  

Software engineers are always looking for new, fast ways to update their models once deployed into production. Whether this involves running a new system, creating a new code, or utilizing a new software, programmers need to find fast and accurate ways to update their models that are already in production in order to maintain efficiency.

Cloud computing has forever changed the way businesses have been storing and managing their data and analytics. Within the past few years, many companies have made the switch from storing their data within a service oriented architecture (SOA) to the cloud.