Operationalizing Data Science Models — DataFactZ

Challenge: The turnaround time between building a model and deployment can be upwards of six months:

Why: The process of deploying machine learning models is challenging, and requires more time and resources than deploying application codes. Common reasons for delays are:

  1. Refactoring the production code to fit a machine learning model can be time-consuming, especially when migrating to a different programming language like Python to Java.

Recommendation: Institute a machine learning operations (MLOps) team and allocate time to creating unique strategies for the deployment of Machine Learning Models. Every model should have a comprehensive data assessment prior to building. This will give an understanding to risk, better environment considerations, and encourage high-quality data sets that limit blockers during testing. A robust deployment approach is also an imperative step to standardizing processes, streamlining testing, and integration. This will allow for a reusable and resilient deployment process that lowers overhead caused by the unpredictability of bad data.

Originally published at https://datafactz.com on February 11, 2022.

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DataFactZ is a global data analytics firm, with cutting edge data driven technology solutions.

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DataFactZ

DataFactZ is a global data analytics firm, with cutting edge data driven technology solutions.