At beggining of 2022, the team behind the Apple’s popular programming language Swift, Swift 6, announced the ambitius plan to open up the programming language for machine learning. It is well known that Swift has already gained the good reputation to be practical en efficeint at the moment to to executing at the production level every project, being by far, faster and more robust in comparasion with other programming langages.
However, and knowing that languages like Java, Python or C++ are extremely useful for Big Data and Machine Learning, why we should consider Swift instead?
Python, so far, is leading the prefered language for big data and machine not only because is easy to handle and manipulate but for its fast learning curve and low cost. And just because the learning curve is an important variable to consider at the momento to choose a language, Swift is getting a fast popularity among the developers to start working with all the spectre of Data Sciencie. Consider too, the fact that there are so many devolopers already working with Swift for similar projects with big databases, that it could be an easier move to keep Swift for their machine learning projects.
Let’s consider other key factors to put Swift over Python:
- Swift is 8 times faster than Python. (Swift and Python) Swift has the advantage to fix errors faster and make it run again, whereas Python has most significant amount of crashes during the run time.
- Exchangable and Interoperability: Swift jsut started with machine learning, it is in a first phase which is more difficult to interoperate with Python and C++ but the opposite, Swift can import all from Python and C++ and operate with own libraries.
- Learning Curve: This is the most important aspect that differenciates Swift among other populars languages for big data. Since Swift has a more sophisticated way to accelerate the learning curve thanks to the resources available in Apple and the simple and eay logic behind the language, makes it more fast to learn and experiment, wheares other languages demand much more time to learn.
On the other hand, Swift still have challenges to overcome, like stability and resources to add to its libraries. This is a common experience among the developers, and also the memory required to start programming big data, which depends, at the of the day, of the capability and memory of a Apple’s computer.