In this ask the expert, ParallelM CTO Nisha Talagala lays out the similarities and differences between traditional software engineering and machine learning.
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. Talagala is the CTO and vice president of engineering at ParallelM Inc., a software startup that builds enterprise software for operationalizing machine learning and data science. Prior to joining ParallelM, she was a fellow at SanDisk, a flash memory product manufacturer; a fellow at Fusion-io, a flash memory technology company; and a technology lead for server flash at Intel. She holds more than four dozen patents.
In this ask the expert, which has been edited for brevity and clarity, Talagala describes how machine learning and software engineering are a similar branch of knowledge. She also explains how machine learning algorithms are pushing beyond the constraints of software engineering and posing new challenges for the enterprise.
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