a general answer is tough since it depends among others on:
- the solution / product architecture you are responsible for
- and which kind of data you are dealing with, see also this thread to determine if classic ML or rather Deeplearning is more suitable: Decision Trees for Regression? - #8 by Christian_Simonis
- on your current tech stack
In addition it depends also:
- where you stand now (beginner | medium | advanced)
- what you want to achieve (e.g. develop a certain capability for your product / solution or even enterprise?)
- your strength and background of the industry you want to work (e.g. background in image processing, working in automotive industry in the automated driving area or so)
- your timeline, considering how much time you want to invest in your learning roadmap (e.g. 5 hours per week for 8 months or so)
@Vaibhav_Sharma15: I understood you are opting for a cloud-native, serverless approach. Can you provide some more info on the data, the use case and the product you are designing the architecture for?
Here some thread for a learning sequence, e.g. on classic machine learning / Deeplearning: Order of courses - #4 by Christian_Simonis
But as I said, I believe it’s more important to ask yourself which capabilities seem most relevant to you based on the use case and the data (e.g. image processing on big unstructured data like video data might be suitable for DL as the technology and you might gonna need a different tech stack compared to a small data use case).
Best regards
Christian