How to Transition into Data Science as an MLOps Engineer?
I am a mid level Software/ML Ops Engineer. In my previous company, I worked on a team that productionized ML models. They were heavily sprint based. The data scientists built the cool models and took ownership of solving the overall use case/problem. My interests have always leaned towards the data science side of things, due to various reasons, I wasn’t able to do much courses or side projects in data science during my previous job. I’m now looking to transition into Data Science/applied science roles (pretty much open to any area, preferably NLP/Gen AI). What’s also appealing to me is that data science itself doesn’t lend itself that well to weekly sprints, due to the nature of the job and tend to go with monthly milestones or updates which I’m more comfortable with. (Correct me if you’ve had a different experience) I had a background in machine learning from before, so I am comfortable with the math and classical ML, CNNs and NLP DL basics like LSTMs etc 1) What is the best way to make this transition in the next few months to an year? I’m more likely to land another MLOps or Infra role in the next few months, but I want to make this transition sooner than later. 2) Thinking of starting with Kaggle projects and revising some basics. Not sure if Kaggle projects will actually help me land a job. If you have any other ideas, I’m all ears. 3) What are some things that surprised you after transitioning into data science/applied scientist after being a software engineer? Any tips or gotchas? #data science #tech #machinelearning #machinelearningengineer
All you need is stats at that point, but honestly data science is trending down these days. That'd be a downgrade
Really surprised. Would love to hear about your experience.
Don’t
Really surprised. Would love to hear about your experience.
It depends. Majority of what current data scientists do is just becoming the data domain expert. O/w they mostly use the off the shelf tools. If you like the actual implementation and development of ml algorithms, then an ml engineering role is much more suitable
I'm in DS and want to be in MLOps
Really surprised. Would love to hear about your experience.
When you leave can I have your job
I wish I did.
To be honest, MLOps is more valuable and involved than DS and ML algos rn. Without MLOps, none of it works. That being said, you’d be super valuable for any DS role given your background.
I however see more roles asking for specific Deep Learning Experience while also asking for productionization skills. People reject me because I don’t have the specific DL experience they’re looking for.
I feel like this is similar to full stack engineers vs backend engineers vs front end. Companies want someone who can do it all, not just a frontend or a backend. Maybe a full stack ML engineer would make the best sense for me?