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Found 12 papers, 3 papers with code
Deploying a Steered Query Optimizer in Production at Microsoft
Modern analytical workloads are highly heterogeneous and massively complex, making generic query optimizers untenable for many customers and scenarios.
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.
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
We present DataScope (ease. ml/datascope), the first system that efficiently computes Shapley values of training examples over an end-to-end ML pipeline, and illustrate its applications in data debugging for ML training.
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.
Phoebe: A Learning-based Checkpoint Optimizer
To address these issues, we propose Phoebe, an efficient learning-based checkpoint optimizer.
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.
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.
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
To this end, we propose a framework that translates a pre-trained ML pipeline into a neural network and fine-tunes the ML models within the pipeline jointly using backpropagation.
Machine Learning at Microsoft with ML .NET
Machine Learning is transitioning from an art and science into a technology available to every developer.
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformations.
