'The Single Biggest Bottleneck For All Machine Learning Is Software Engineering'

Interview with Nikhil Garg, Software Engineering Manager at Quora


Founded eight years ago, question-and-answer site Quora has grown into one of the most visited sites on the internet. It now has 190 million monthly users, up from 100 million a year ago, and recently completed a funding round that saw it valued at $1.8 billion.

At the heart of this growth has been an obsession with data, and some unique innovations in the field of machine learning. Quora uses machine learning in several ways, from ranking answers to getting answers from experts. At the heart of these efforts is engineering manager, Nikhil Garg. He leads a team of great engineers working in the intersection of machine learning and infrastructure. His team is responsible for building out centralized systems, standardized frameworks, and powerful tools that enable the rest of the company to do machine learning.

We sat down with Nikhil ahead of his presentation at the Machine Learning Innovation Summit, taking place this June 5-6 at the Marriott Union Square in San Francisco.

Where do you think machine learning’s most important applications will be in the near future?

It's hard to pin-point one area where ML will have important applications. In fact, the most exciting promise of ML to me is that its impact won't be limited to a single vertical or area but rather would be felt in an extremely wide variety of economic sectors - all the way from retail, manufacturing, and logistics to education, healthcare and everything in between.

In a recent KDnuggets poll, 51% of respondents said that they expect most expert-level Predictive Analytics/Data Science tasks currently done by human Data Scientists to be automated to happen within the next decade. Do you think the data scientist’s role is really under threat? What kind of impact do you think it will have on the job market in general and are governments prepared?

I don't think that the role of a data scientist will be under threat anytime soon. It is true that similar to everything else within tech, automation, and abstraction within ML will slowly climb to higher and higher levels. As a result, we'd need fewer data scientists and ML engineers in a few years for the same task compared to what we do. It's just that the total number of things that would need to be done using ML would continue rising up at a faster rate. As a result, I think that the total demand (and consequently the supply) for people with machine learning skills and background will skyrocket in the next decade.

Are there any new technologies or ideas in the machine learning space that you find particularly exciting or believe will be especially important in the next few years?

I'm very excited to see more progress in the area of end-to-end differentiable network architectures. I feel that our rate of innovation will greatly accelerate if we could somehow also offload the learning of network architectures to machines.

What challenges do you foresee holding back machine learning from achieving its potential? How do you think these could be overcome?

I think most would agree that the single biggest bottleneck for all machine learning is software engineering. We all collectively in the tech industry are still figuring out the best practices, tools, abstractions, and systems that can enable large organizations to innovate in ML at a huge data scale.

What will you be discussing in your presentation?

One of my teams at Quora is responsible for building out our ML Platform. As part of this, I've spent a lot of time thinking about the ideal systems to make ML development lot easier. Unfortunately, these systems don't exist in the open source, not yet anyway. In this talk, I will be discussing some ML systems, that in my view, will be fairly general and useful to a lot of people and hence need to be built. 

You can hear more from Nikhil, along with other leading experts in the field from the likes of Google, Amazon, and Facebook, at the Machine Learning Innovation Summit. View the full agenda here.

BONUS CONTENT: Danny Lange, Head of Machine Learning at Uber, discusses how the ride sharing giant uses machine learning and its potential for your organization at the 2016 Machine Learning Innovation Summit



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