Duolingo is the world’s most popular language learning app with more than 150 million users (at the time of this writing). At the core of the Duolingo experience, users learn in bite-sized lessons which consists of several interactive exercises (internally, we call them “challenges”).

So, for any given lesson, which exercises should a user see and in what order? This is the responsibility of Session Generator, our backend module which gets data from one of our 88 language courses (and counting!) in the Duolingo Incubator, sprinkles some machine learning magic, and proceeds to serve a sequence of exercises tailored to the needs of each of our millions of users.

Qunar 智能风控场景中，风控研发团队经常会应用一些算法模型，来解决复杂场景问题。典型的如神经网络模型，决策树模型等等。而要完成模型从训练到部署预测的全过程，除了模型算法之外，离不开技术框架的支撑。本篇文章将和大家分享一下，在预测服务部署阶段，基于 Tensorflow for Java 和 Spark-Scala 构建分布式机器学习计算框架的实践经验。