Thread by @Aurimas_Gr

Considering switching to a 𝗠𝗟𝗢𝗽𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 role?

My thought in the 🧵

#Data #DataEngineering #MLOps #MachineLearning #DataScience

Usually MLOps Engineers are professionals tasked with building out the ML Platform in the organization.

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This means that the skill set required is very broad - naturally very few people start off with the full set of skills you would need to brand yourself as a MLOps Engineer. This is why I would not choose this role if you are just entering the market.

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However, you can get into the role from various sides once you already have some experience. Here are some examples - If you currently are:

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ML Engineer: if you are currently successful in your role you might already have skills that are needed to become a successful MLOps Engineer. What you will need to do though is switch from an execution to a service role. So the main shift is mental rather than technical.

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𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲/𝗖𝗹𝗼𝘂𝗱 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: you are most likely good with infrastructure architecture, IaaC, Cloud Services etc. These are all crucial skills to have in the ML platform team.

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𝗗𝗲𝘃𝗢𝗽𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: you have probably mastered CI/CD infrastructure and very well know how to template and automate things, how to increase developer velocity - each of these being a necessity to become MLOps engineer.

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Software Engineer: areas that software engineering skills could be leveraged in ML platform team: coding up clean interfaces, backend services, UIs to be used by platform adopters. Additionally, you are probably as good with CI/CD infrastructure as most DevOps engineers are.

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In any case - be prepared to learn a lot as MLOps Engineers are generalists and it usually takes a lot of time to acquire the full-stack.

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👋 I am Aurimas.

I will help you Level Up in #MLOps, #MachineLearning, #DataEngineering, #DataScience and overall #Data space.

𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 and hit 🔔

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