Source: towardsdatascience.com
Thirty-One Checks For Different Levels of Machine Learning Operation MaturiCategory: Microsoft, Kubernetes, apple, automation, artificial-intelligence
As part of the AI 0 surge (1983–1987), I felt that AI, to be of practical use, had to be distributed.
I admit I stopped when I encountered Kubernetes in 2016 because from a Software Architect and Engineer viewpoint, there was little I could add, and what I could add would be minor.
Symptoms at level 3 that need solution are: Solutions at MLOps Maturity Level 3 are (11 checklist items): Computational resources can include but are not limited to CPUs, memory, storage, operating systems, and IP addresses.
I have not been in an organization yet that is at MLOps Maturity Level I have read articles or seen open-source code that leads me to believe that some organizations are at MLOps Maturity Level 3 or MLOps Maturity Level Those organizations with many users or several deployments per week need to achieve MLOps Maturity Level 4. Solutions at MLOps Maturity Level 4 are (6 checklist items): You don’t have a choice to have logs implemented other than a stream of events.
I admit I stopped when I encountered Kubernetes in 2016 because from a Software Architect and Engineer viewpoint, there was little I could add, and what I could add would be minor.
Symptoms at level 3 that need solution are: Solutions at MLOps Maturity Level 3 are (11 checklist items): Computational resources can include but are not limited to CPUs, memory, storage, operating systems, and IP addresses.
I have not been in an organization yet that is at MLOps Maturity Level I have read articles or seen open-source code that leads me to believe that some organizations are at MLOps Maturity Level 3 or MLOps Maturity Level Those organizations with many users or several deployments per week need to achieve MLOps Maturity Level 4. Solutions at MLOps Maturity Level 4 are (6 checklist items): You don’t have a choice to have logs implemented other than a stream of events.
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