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Found 11 papers, 1 papers with code
Sibyl: Forecasting Time-Evolving Query Workloads
Drawing insights from real-workloads, we propose template-based featurization techniques and develop a stacked-LSTM with an encoder-decoder architecture for accurate forecasting of query workloads.
MotherNet: A Foundational Hypernetwork for Tabular Classification
In contrast to existing hypernetworks that were either task-specific or trained for relatively constraint multi-task settings, MotherNet is trained to generate networks to perform multiclass classification on arbitrary tabular datasets without any dataset specific gradient descent.
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.
A Tensor Compiler for Unified Machine Learning Prediction Serving
Machine Learning (ML) adoption in the enterprise requires simpler and more efficient software infrastructure---the bespoke solutions typical in large web companies are simply untenable.
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.
MLOS: An Infrastructure for Automated Software Performance Engineering
In this paper we present: MLOS, an ML-powered infrastructure and methodology to democratize and automate Software Performance Engineering.
Data Science through the looking glass and what we found there
The recent success of machine learning (ML) has led to an explosive growth both in terms of new systems and algorithms built in industry and academia, and new applications built by an ever-growing community of data science (DS) practitioners.
Extending Relational Query Processing with ML Inference
The broadening adoption of machine learning in the enterprise is increasing the pressure for strict governance and cost-effective performance, in particular for the common and consequential steps of model storage and inference.
Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML
Consequently, rigorous data management has emerged as a key requirement in enterprise settings.
Griffon: Reasoning about Job Anomalies with Unlabeled Data in Cloud-based Platforms
In contrast, in Griffin we cast the problem to a corresponding regression one that predicts the runtime of a job, and show how the relative contributions of the features used to train our interpretable model can be exploited to rank the potential causes of job slowdowns.
Towards Geo-Distributed Machine Learning
Current solutions to learning from geo-distributed data sources revolve around the idea of first centralizing the data in one data center, and then training locally.
