Data collection and quality challenges in deep learning: A data-centric ai perspective
Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …
learning becomes the new software, powered by big data and computing infrastructure …
[HTML][HTML] The pipeline for the continuous development of artificial intelligence models—Current state of research and practice
Companies struggle to continuously develop and deploy Artificial Intelligence (AI) models to
complex production systems due to AI characteristics while assuring quality. To ease the …
complex production systems due to AI characteristics while assuring quality. To ease the …
Operationalizing machine learning: An interview study
S Shankar, R Garcia, JM Hellerstein… - ar** machine learning models can be seen as a process similar to the one
established for traditional software development. A key difference between the two lies in the …
established for traditional software development. A key difference between the two lies in the …
Management of machine learning lifecycle artifacts: A survey
The explorative and iterative nature of develo** and operating ML applications leads to a
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …
variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software …
Ggfast: Automating generation of flexible network traffic classifiers
When employing supervised machine learning to analyze network traffic, the heart of the
task often lies in develo** effective features for the ML to leverage. We develop GGFAST …
task often lies in develo** effective features for the ML to leverage. We develop GGFAST …
Riding a bicycle while building its wheels: the process of machine learning-based capability development and IT-business alignment practices
Purpose Recent advancements in Artificial Intelligence (AI) and, at its core, Machine
Learning (ML) offer opportunities for organizations to develop new or enhance existing …
Learning (ML) offer opportunities for organizations to develop new or enhance existing …
Hyper-tune: Towards efficient hyper-parameter tuning at scale
The ever-growing demand and complexity of machine learning are putting pressure on
hyper-parameter tuning systems: while the evaluation cost of models continues to increase …
hyper-parameter tuning systems: while the evaluation cost of models continues to increase …
Openbox: A Python toolkit for generalized black-box optimization
Black-box optimization (BBO) has a broad range of applications, including automatic
machine learning, experimental design, and database knob tuning. However, users still face …
machine learning, experimental design, and database knob tuning. However, users still face …