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Found 6 papers, 3 papers with code
End-to-end Optimization of Machine Learning Prediction Queries
First, it employs logical optimizations that pass information between the data part (and the properties of the underlying data) and the ML part to optimize each other.
Query Processing on Tensor Computation Runtimes
Finally, TQP can accelerate queries mixing ML predictions and SQL end-to-end, and deliver up to 9$\times$ speedup over CPU baselines.
Lights, Camera, Action! A Framework to Improve NLP Accuracy over OCR documents
Document digitization is essential for the digital transformation of our societies, yet a crucial step in the process, Optical Character Recognition (OCR), is still not perfect.
Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation
Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost.
Large-Scale Intelligent Microservices
Deploying Machine Learning (ML) algorithms within databases is a challenge due to the varied computational footprints of modern ML algorithms and the myriad of database technologies each with its own restrictive syntax.
MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales
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.
