Why it’s important for AI leaders to keep their coding skills
When you get promoted as a data scientist, you will either lead the AI project team on the business side or the ML engineering team on the IT side. Regardless, you need to keep your coding skills.
I was promoted recently as an AI/ML technical lead on the IT side. Since being promoted, I have not needed to code at work, as I have developers to configure and optimize the AI tools and AI stack on the data science platform side, and data scientists who build AI prototypes and solutions on the project side. However, my coding skills and experience come in handy as a new AI/ML technical lead, as my coding skills are what informs my thinking when setting the AI/ML vision for IT.
I’m the first AI/ML technical lead for my organization, and that in itself is a milestone. But without the coding skills and the experience in building AI prototypes and solutions, I would not be as effective in leading these technical AI teams.
Case in point, as an Enterprise Data Science leader, you will be expected to contribute to the practice standards for Enterprise Data Science, and contribute to a framework for how data science is delivered at your organization. Within this Enterprise Data Science framework, you are expected as the AI/ML technical lead to compile all of the AI projects that will be built and deployed in production, then with these requirements, you are expected to architect, build and optimize an Enterprise Data Science Platform that allows for these AI projects to be delivered at scale, at Enterprise.
Without coding skills and the experience of building AI solutions, then how do you build and optimize your Enterprise Data Science platform? How do you know how to maintain the integrity of the Machine Learning Development Life Cycle (MLDLC) when iterating on building and operationalizing an AI solution, if you don’t code and train, test, and deploy ML models? How do you work with a multidisciplinary AI team (business SME, Enterprise Data Scientist, data engineer, ML engineer, ML test engineer, MLOps engineer, ML architect, model owner) and work in an MLOps framework, if you don’t code, and have to oversee this highly technical AI team? When someone asks for AI peer review, how do you look at their approach and techniques if you can’t follow their code? When a key AI team member is out on leave, how can you fill in the gaps if you don’t code, as either you have to fill the gap yourself or have the knowledge enough of where this coding resource may come from in your organization. And when you look at the big picture, you can’t really zoom out for the big picture unless you are also able to zoom in as needed, reading and optimizing code.
At the end of the day, as a newly promoted Enterprise Data Scientist in your organization, it is very important to keep and build your coding skills. There’s a reason you were promoted- you have already demonstrated your abilities and experience in delivering on AI projects. Now that you are the AI/ML technical lead, you need to maintain and improve upon the skills that got you there, which is your coding skills.
Although I don’t code daily anymore at work, those coding skills and experience comes in handy everyday I have to lead those AI teams, with the issues I detailed above. To keep my coding skills and learn new techniques and ML algorithms, I build personal AI projects on my own time, code AI projects on my personal gaming laptop, and read up on the latest on Enterprise Data Science.
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Take home message- keep your coding skills as an AI/ML technical lead, and keep building your coding knowledge to keep up with the next trends in Enterprise Data Science.
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