We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications.
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation.
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
Recent advances in machine learning are consistently enabled by increasing amounts of computation.
Federated learning (FL) is a rapidly growing research field in machine learning.
Modern learning models are characterized by large hyperparameter spaces and long training times.
The scale of modern datasets necessitates the development of efficient distributed optimization methods for machine learning.
In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.