Accelerating human-in-the-loop machine learning: Challenges and opportunities
Development of machine learning (ML) workflows is a tedious process of iterative
experimentation: developers repeatedly make changes to workflows until the desired …
experimentation: developers repeatedly make changes to workflows until the desired …
Data management in machine learning: Challenges, techniques, and systems
Large-scale data analytics using statistical machine learning (ML), popularly called
advanced analytics, underpins many modern data-driven applications. The data …
advanced analytics, underpins many modern data-driven applications. The data …
Automatic database management system tuning through large-scale machine learning
Database management system (DBMS) configuration tuning is an essential aspect of any
data-intensive application effort. But this is historically a difficult task because DBMSs have …
data-intensive application effort. But this is historically a difficult task because DBMSs have …
Data lifecycle challenges in production machine learning: a survey
Machine learning has become an essential tool for gleaning knowledge from data and
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …
tackling a diverse set of computationally hard tasks. However, the accuracy of a machine …
AI meets database: AI4DB and DB4AI
Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …
make database more intelligent (AI4DB). For example, traditional empirical database …
[HTML][HTML] Data management for production quality deep learning models: Challenges and solutions
Deep learning (DL) based software systems are difficult to develop and maintain in industrial
settings due to several challenges. Data management is one of the most prominent …
settings due to several challenges. Data management is one of the most prominent …
Systemml: Declarative machine learning on spark
M Boehm, MW Dusenberry, D Eriksson… - Proceedings of the …, 2016 - dl.acm.org
The rising need for custom machine learning (ML) algorithms and the growing data sizes
that require the exploitation of distributed, data-parallel frameworks such as MapReduce or …
that require the exploitation of distributed, data-parallel frameworks such as MapReduce or …
Database meets artificial intelligence: A survey
Database and Artificial Intelligence (AI) can benefit from each other. On one hand, AI can
make database more intelligent (AI4DB). For example, traditional empirical database …
make database more intelligent (AI4DB). For example, traditional empirical database …
Probabilistic demand forecasting at scale
We present a platform built on large-scale, data-centric machine learning (ML) approaches,
whose particular focus is demand forecasting in retail. At its core, this platform enables the …
whose particular focus is demand forecasting in retail. At its core, this platform enables the …
Keystoneml: Optimizing pipelines for large-scale advanced analytics
ER Sparks, S Venkataraman, T Kaftan… - 2017 IEEE 33rd …, 2017 - ieeexplore.ieee.org
Modern advanced analytics applications make use of machine learning techniques and
contain multiple steps of domain-specific and general-purpose processing with high …
contain multiple steps of domain-specific and general-purpose processing with high …