AI Professionals’ Inclination towards Kotlin: Is it Justified?

Data science is a booming field, it’s offering businesses what they had been always been seeking for years — a distinct competitive advantage. Imagine, as a business entity, you have a large pool of business-specific data that you can use to extract insights from. Leveraging data analytics tactics, you can find patterns in the available data, and the basis that, can draw some useful business insights. If you are new to data science, consider it as a field of study that deals in data analysis, data engineering, data visualization, machine learning, and other similar concepts.

Let’s consider an example, you have a dataset that is huge, let’ say 100 GB, it will take around 10 minutes to load this amount of data. In case, you make an error in the code, you will need to redo it. You are again going to wait for 10 minutes. In the said scenario, it would make a lot more sense to deploy ‘Jupyter Notebooks’ here. You can perform a code cell, interpret the output, and later, run another code cell. To execute the same in R or Python, you need the aid of Jupyter Notebooks. Although a new programming language, namely ‘Kotlin’ has emerged off late, that makes ML (machine learning) coding simpler than ever.

What’s Kotlin?

Kotlin is a new coding language for developing machine learning algorithms, and its a target, namely JVM, has kept its place among the majorly leveraged execution environments for software programming. We know, for a fact, that programming is changing with the soaring adoption of AI technologies, and more particularly, deep learning approaches. This is one area where JVM falls short in terms of not being able to compete sturdily because of the absence of library access.

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