Software Engineering or Data Science: what to get into depends on what you want to get out
Should you be a software developer or get into Data Science and learn AI?
Everyone and their dog has heard of AI and many want to use it to solve a problem. It seems as though the practical application of “learning AI” depends on what you want to get out. Let’s explore what that looks like.
Useful information vs Usefulness
What people may not know is that the vast majority of data scientists and ML engineers work on discovery projects, prototypes, and research. Much of which is extremely interesting but very few actually deploy them into the real world. Often details cannot be discussed let alone published. However, when published, it can provide very useful information. Nevertheless, it may be less useful to others if the trained models are never deployed into a consumer application.
As a Developer, I deciphered that it makes sense for me to leverage AI into applications. Granted that if you’re not coming from a software engineering background, your thinking process will most likely differ.
The main tenet was that a lot can be done in AI but if I’m not going to get deep into the models and create my own learning algorithms and all I’ll be doing is leveraging the existing ones and ML libraries then perhaps I’m better off being a developer who is able to employ those into their applications. Concluding that ultimately, if I’m not deploying models to production to solve problems then perhaps I’d be better off deploying apps that creatively use preexisting ones.
Let’s list a few of the main libraries that developers are using in their applications to create wonderful AI-powered applications:
Mobile-specific libraries:
This is to name a select few in this vast ecosystem. There are also a plethora of different language-specific libraries that are lesser well known that can be used.
Hope thinking of the desired output helps your decision making if you’re also torn between the two. If so, let me know in the comments down below!
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