Facebook today is talking for the first time about FBLearner Flow, a piece of software that manages machine learning models for employees throughout the social networking company. It's similar in some ways to cloud-based machine learning services like Microsoft's Azure Machine Learning, or the open-source Airflow from Airbnb, but it's optimized for Facebook's own infrastructure and filled with algorithms developed by Facebook engineers, so that many other people inside the company can make use of them for their own models. "More than 25 percent of the company are using it, in scenarios that we haven't really envisioned before," Hussein Mehanna, director of Facebook's Core Machine Learning Group, told VentureBeat in an interview. Facebook could go beyond just publishing academic papers on FBLearner Flow; the company may well end up open-sourcing it. Facebook engineers have discussed this type of technology with people at LinkedIn, Twitter, and Uber, and they've all been excited about it, Mehanna said.
Salesforce CEO Marc Benioff said at a recent conference: "This is a huge shift going forward, which is that everybody wants systems that are smarter, everybody wants systems that are more predictive, everybody wants everything scored, everybody wants to understand what's the next best offer, next best opportunity, how to make things a little bit more efficient." Machine Learning (ML) and AI powering "Systems that Learn at scale" are at the bleeding edge of data science, deep learning and predictive search today. Everyone is jumping on this AI enabled engagem ent ("ambient reality, experience and convenience") trend in retail, banking and even healthcare. Facebook is a case study of where AI/ML are being used to augment user engagement and experiences. I am starting to see many leading firms investing in ML Accelerators and Platforms as part of their data science strategy.
Many of the experiences and interactions people have on Facebook today are made possible with AI. When you log in to Facebook, we use the power of machine learning to provide you with unique, personalized experiences. Machine learning models are part of ranking and personalizing News Feed stories, filtering out offensive content, highlighting trending topics, ranking search results, and much more. There are numerous other experiences on Facebook that could benefit from machine learning models, but until recently it's been challenging for engineers without a strong machine learning background to take advantage of our ML infrastructure. In late 2014, we set out to redefine machine learning platforms at Facebook from the ground up, and to put state-of-the-art algorithms in AI and ML at the fingertips of every Facebook engineer.
It's no longer up for debate that AI is set to have a major impact on most businesses, if it isn't already--and any company that wants to stay ahead must figure out how to integrate the new technology into its structure. But how is a successful AI platform built? Perhaps best known as the guy who introduced Steve Jobs and Steve Wozniak, Bill Fernandez speaks out on Apple's founding magic, how love built the first Mac, and the interface of the future. In Mehanna's session, he explained how Facebook developed its own machine learning platform, and how Facebook employees are using it. In 2012, Mehanna said, Facebook's AI platform was "a snowball of complexity"--a system that slowed progress down significantly.
It's no longer up for debate that AI is set to have a major impact on most businesses, if it isn't already--and any company that wants to stay ahead must figure out how to integrate the new technology into its structure. But how is a successful AI platform built? How the'PayPal Mafia' redefined success in Silicon Valley A decade ago, the PayPal Mafia played a major role in revitalizing the tech industry in Silicon Valley. The story behind this group of leaders proves that their success is more than just luck. In Mehanna's session, he explained how Facebook developed its own machine learning platform, and how Facebook employees are using it.