Am going through this exact transition. Some key learnings I had:
1. Front end is customer obsessed. Trying to think through all customer navigation paths and design UI covering all aspects of usability. ML engineering, many times, is not customer facing. There will always be times when you don't know how your product / algorithm is put to use. Be prepared to accept such a change.
2. ML engineering is a lot about data - raw and unstructured. Your flexibility to work with any kind of data, being creative to come up data related processes is important.
3. Front end is overloaded with frameworks for improving developer productivity. ML frameworks have just set the foundation and your contributions at this stage to some of these will help it to grow stronger.
4. Both front-end and ML need deeper understanding of the domain on which it is applied to. Understanding every business logic involved will help to build better algorithms as well as front end workflows.
5. Lastly, ML too need a lot of front end tools to capture data, annotate data, cleanup data, measure model outcome etc. So, in lots of areas Frontend and ML go hand-in-hand and knowing both would be a unique skill-set in the market.
1. Front end is customer obsessed. Trying to think through all customer navigation paths and design UI covering all aspects of usability. ML engineering, many times, is not customer facing. There will always be times when you don't know how your product / algorithm is put to use. Be prepared to accept such a change.
2. ML engineering is a lot about data - raw and unstructured. Your flexibility to work with any kind of data, being creative to come up data related processes is important.
3. Front end is overloaded with frameworks for improving developer productivity. ML frameworks have just set the foundation and your contributions at this stage to some of these will help it to grow stronger.
4. Both front-end and ML need deeper understanding of the domain on which it is applied to. Understanding every business logic involved will help to build better algorithms as well as front end workflows.
5. Lastly, ML too need a lot of front end tools to capture data, annotate data, cleanup data, measure model outcome etc. So, in lots of areas Frontend and ML go hand-in-hand and knowing both would be a unique skill-set in the market.
Good luck on expanding your domain to ML.