Synapse Machine Learning expands the distributed computing framework Apache Spark in several new directions. SynapseML adds several machine learning frameworks to the SparkML Ecosystem, including LightGBM, Vowpal Wabbit, OpenCV, Isolation Forest, and the Microsoft Cognitive Toolkit (CNTK). These tools allow users to craft powerful and highly scalable models that span multiple ML ecosystems.
The process of deploying Machine Learning (ML) algorithms within databases is challenging. The varied computational footprints of modern ML algorithms and the myriad of database technologies, each with their own restrictive syntax, make such tasks more than a little complex. We introduce an Apache Spark-based micro-service orchestration
If someone asked you what snow leopards and Vincent Van Gogh have in common, you might think it was the beginning of a joke.
"Integrating the power of Azure Cognitive Services into your big data workflows on Apache Spark™
Today at Spark + AI Summit 2019, we're excited to introduce a new set of models in the SparkML ecosystem that make it easy to use Azure Cognitive Services at terabyte scales.
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library. The library tackles problems in Deep Learning, Micro-Service Orchestration, Gradient Boosting, Model Interpretability, and other areas of modern computation.
In this work, we detail a novel open source library called MMLSpark that combines the flexible deep learning library Cognitive Toolkit with the distributed computing framework Apache Spark. To achieve this union, we have contributed Java Language bindings to the Cognitive Toolkit