Search Results for author: Dalitso Banda

Found 6 papers, 3 papers with code

End-to-end Optimization of Machine Learning Prediction Queries

no code implementations31 May 2022 Kwanghyun Park, Karla Saur, Dalitso Banda, Rathijit Sen, Matteo Interlandi, Konstantinos Karanasos

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.

BIG-bench Machine Learning

Query Processing on Tensor Computation Runtimes

no code implementations3 Mar 2022 Dong He, Supun Nakandala, Dalitso Banda, Rathijit Sen, Karla Saur, Kwanghyun Park, Carlo Curino, Jesús Camacho-Rodríguez, Konstantinos Karanasos, Matteo Interlandi

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

1 code implementation6 Aug 2021 Amit Gupte, Alexey Romanov, Sahitya Mantravadi, Dalitso Banda, Jianjie Liu, Raza Khan, Lakshmanan Ramu Meenal, Benjamin Han, Soundar Srinivasan

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.

named-entity-recognition Named Entity Recognition +3

Large-Scale Intelligent Microservices

1 code implementation17 Sep 2020 Mark Hamilton, Nick Gonsalves, Christina Lee, Anand Raman, Brendan Walsh, Siddhartha Prasad, Dalitso Banda, Lucy Zhang, Mei Gao, Lei Zhang, William T. Freeman

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.

Anomaly Detection

MMLSpark: Unifying Machine Learning Ecosystems at Massive Scales

1 code implementation20 Oct 2018 Mark Hamilton, Sudarshan Raghunathan, Ilya Matiach, Andrew Schonhoffer, Anand Raman, Eli Barzilay, Karthik Rajendran, Dalitso Banda, Casey Jisoo Hong, Manon Knoertzer, Ben Brodsky, Minsoo Thigpen, Janhavi Suresh Mahajan, Courtney Cochrane, Abhiram Eswaran, Ari Green

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.

BIG-bench Machine Learning Distributed Computing +2

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