Source: towardsdatascience.com
Why does your Machine Learning Ops feel inadequate?Category: Data
An early item in this list is about setting up CI/CD for the new service.
I consider how it eventually gets deployed to production and how it gets monitored post-deployment.
However, when I started the MLOps journey for our company, I realized that CI/CD of the kind practiced in software engineering is not a standard in Data Science practices.
This role is often used in the context of scaling the data that goes into the pipeline(ETL, Spark, etc) and not the ML model-related problems.
When this happened to us, we set forth in search of a provider who did MLOps out of the box.
I consider how it eventually gets deployed to production and how it gets monitored post-deployment.
However, when I started the MLOps journey for our company, I realized that CI/CD of the kind practiced in software engineering is not a standard in Data Science practices.
This role is often used in the context of scaling the data that goes into the pipeline(ETL, Spark, etc) and not the ML model-related problems.
When this happened to us, we set forth in search of a provider who did MLOps out of the box.
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