activity
20192025
most citedMachine Learning at Microsoft with ML .NET

53 citations · 69 across the 5 of their papers we have counts for

collaborators

5 papers

cs.DB2025★ 1 cited

Streaming Democratized: Ease Across the Latency Spectrum with Delayed View Semantics and Snowflake Dynamic Tables

Daniel Sotolongo, Daniel Mills, Tyler Akidau +24

Streaming data pipelines remain challenging and expensive to build and maintain, despite significant advancements in stronger consistency, event time semantics, and SQL support ove…

cs.DC2023★ 5 cited

Runtime Variation in Big Data Analytics

Yiwen Zhu, Rathijit Sen, Robert Horton +2

The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience…

cs.DB2021★ 7 cited

Phoebe: A Learning-based Checkpoint Optimizer

Yiwen Zhu, Matteo Interlandi, Abhishek Roy +5

Easy-to-use programming interfaces paired with cloud-scale processing engines have enabled big data system users to author arbitrarily complex analytical jobs over massive volumes…

cs.DC2020★ 3 cited

Seagull: An Infrastructure for Load Prediction and Optimized Resource Allocation

Olga Poppe, Tayo Amuneke, Dalitso Banda +23

Microsoft Azure is dedicated to guarantee high quality of service to its customers, in particular, during periods of high customer activity, while controlling cost. We employ a Dat…

cs.LG2019★ 53 cited

Machine Learning at Microsoft with ML .NET

Zeeshan Ahmed, Saeed Amizadeh, Mikhail Bilenko +31

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate t…