- The platform supports numerous machine learning algorithms and innovative combinations of these algorithms.
- Workflows : A workflow is a single pipeline defined within FBLearner Flow and is the entry point for all machine learning tasks.
- The FBLearner Flow UI provides a few additional experiences: 1) launching workflows, 2) visualizing and comparing outputs, and 3) managing experiments.
Read the full article, click here.
@fchollet: “In a parallel universe, Jeff Dunn introduces LearnerFlow, the Python-based machine learning framework from Facebook.”
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.
Introducing FBLearner Flow: Facebook’s AI backbone | Engineering Blog | Facebook Code | Facebook