How do machine learning algorithms differ from traditional algorithms? | Ask the Experts - TechTarget

In this ask the expert, Nisha Talagala, CTO/VP Engineering at ParallelM, lays out the similarities and differences between traditional software engineering and machine learning, as TechTarget reports.

She also explains how machine learning algorithms are pushing beyond the constraints of software engineering and posing new challenges for the enterprise.

How is machine learning like and unlike software engineering? It's a question that seems to be growing in popularity these days.

Perhaps that's because the bones of machine learning algorithms and traditional algorithms are the same -- they're both code. That's one of the points Nisha Talagala made when we posed the question to her...

From your vantage point as a software development expert, what do you see as the key similarities and differences between machine learning algorithms and traditional algorithms?

Nisha Talagala: At the most basic level, machine learning programs are code. So, they're code written in Python or Java or some programming language. And many people have chosen to put their code in source control repositories like Git. So, at that level, it is similar.
Additionally, they share some stages of code development. For example, in typical code development, there's a development situation, and then you've got a QA-like situation, then you have some preproduction staging and then you have production.
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Source: TechTarget