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LogoutWell for someone who has spent over a year to master the skills needed to become a junior data scientist, the thought of ML flow and sage maker automating everything in data science can be very daunting. However, fear not! Following problems still persists for at least next 10 years, if not more.
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Data is not good: You can’t pass in rubbish to automatic models. So you will definitely need exploratory data analysis and a bit of handson your notebooks. The auto ML tool will definitely cut down your time of building a model or tuning it, but it’ll not take up your job.
2. Data science use cases are not obvious: How to use data science, whether you will go for a statistical distribution based models or machine learning models to give insights is absolutely unclear to the business world. You’d work as an analytics translator here with higher experience. No auto applications can help here.
3. Deployment / AI based models is not a fancy game: This requires serious software skills which most of the data scientist have capability of building. You’d be working with a full stack developer or maybe a devops engineer to get your “auto built” models out.