PODCAST: Machine Learning, AgTech and Tensorflow

TensorFlow's Director of Engineering stops by STACK That to discuss how machine learning is going mainstream

The age of highly accessible, open source machine learning tools is upon us. No longer niche, everyone - from data scientists to Japanese cucumber farmers - is using machine-learning technologies. But what is machine learning?

Machine learning is exactly what it sounds like - software that can learn to solve a problem. Using large sets of data, an algorithm can be trained to understand that data. For example, if given enough data about key attributes which influence housing prices, the algorithm should be able to predict the price of a house based on its various properties.

Today on STACK That, we're talking to Rajat Monga, Director of Engineering of TensorFlow, one of the companies crusading to make machine learning available to everyone.

TensorFlow is one of the most popular machine learning libraries currently available, and for good reason. It's fast, highly readable, supports many types of distributions, and is incredibly well supported by Google. Since its open sourcing in 2015, it has become one of the most forked projects on GitHub and has over 1000 active community contributors.

To teach machines to learn, you need programs to do the trainings. TensorFlow is one of the tools that helps that, says RajatMonga, TensorFlow's Director of Engineering.

There's no better example of TensorFlow's ease of use than the story of Japanese engineer Makoto Saike. Makoto was able to build a cucumber sorting assembly line with TensorFlow all by himself. Makoto used TensorFlow to train an algorithm which recognized the various types of cucumbers grown on his parents' farm and sorted them accordingly. This process massively reduced the amount of labor for his mother, who previously would have to spend all day sorting the cucumbers manually. With this new system in place, his parents were able to focus their efforts on growing more and better vegetables.

As long as you have the data, businesses can start using TensorFlow to identify patterns and recognize trends. Insurance companies can predict the risk factor of their customers. An e-commerce company might use it to predict customer churn or fraudulent transactions. The sky is the limit.

And it's not just farmers or small business owners who can make the most of their data using machine learning. Artificial intelligence, as a whole, enables businesses to increase efficiency and better adapt to an increasingly hybrid IT world. For example, ahead of Discover 2017 Madrid, HPE introduced the industry's first artificial intelligence recommendation engine designed to simplify and reduce the guesswork in managing infrastructure and improve application reliability.

"HPE InfoSight marks the first time a major storage vendor has been able to predict issues and proactively resolve them before a customer is even aware of the problem," said Bill Philbin, Senior Vice President, HPE GM Storage.

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OUR GUEST

Rajat Monga, Director of Engineering at TensorFlow

Rajat Monga leads TensorFlow at the Google Brain team, powering machine learning research and products worldwide. As a founding member of the team he has been involved in co-designing and co-implementing DistBelief and more recently TensorFlow, an open source machine learning system. Prior to this role, he led teams in AdWords, built out the engineering teams and co-designed web scale crawling and content-matching infrastructure at Attributor, co-implemented and scaled eBay's search engine and designed and implemented complex server systems across a number of startups. Rajat received a B.Tech. in Electrical Engineering from Indian Institute of Technology, Delhi.

OUR HOSTS

Florian Leibert, Co-Founder & CEO at Mesosphere

Florian Leibert co-founded Mesosphere in 2013 and currently serves as CEO. Since 2013, The company makes Mesosphere DC/OS, which allows webscale and enterprise companies to quickly deliver containerized, data-intensive applications on any infrastructure. Florian is passionate about building IoT infrastructure and enjoys discussing the benefits of the modern application stack as well as how Mesosphere customers are building world-changing technology. He is also the main creator of Marathon, an orchestrator and scheduler that operates on top of the Mesos software. Florian Leibert drove fundraising efforts through four rounds of financing from prominent investors like Khosla Ventures and Andreessen Horowitz. In addition to his role at Mesosphere, Florian Leibert serves as an investor with Away and Driftt.

Prior to co-founding Mesosphere, Florian Leibert held engineering leadership positions at prominent Internet sites, including Airbnb, Twitter, Ning, and Adknowledge. At age 16, he co-founded online marketplace Knaup Multimedia in Germany.

Byron Reese, CEO & Publisher at Gigaom and CEO at Knowingly

With 25 years as a successful tech entrepreneur, with multiple IPOs and exits along the way, Byron Reese is uniquely suited to comment on the transformative effect of technology on the workplace and on society at large.

As a successful serial entrepreneur and award-winning futurist, Byron balances two unique perspectives. As a futurist, he leads his audience to explore the unprecedented technological changes destined to trigger the dramatic transformation of society. And as an entrepreneur, he shares how to profit from this change while still meeting the practical realities of operating a business.

Speaking across the globe, Byron brings great enthusiasm and talent for deciphering our common destiny and unlocking business opportunities within it. Byrons keynotes and appearances include the PICNIC Festival in Amsterdam, SXSW, the TEDx Austin, Wolfram Data Summit, Spartina, and the IEEE Global Humanitarian Technology Conference, among others.

Byron is also the publisher of Gigaom. A role enhanced by 20 + years' experience building and running successful Internet and software companies. Of the five companies he either started or joined early, two went public, two were sold, and one resulted in a merger.

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