Machine learning operations (mlops): Overview, definition, and architecture
D Kreuzberger, N Kühl, S Hirschl - IEEE access, 2023 - ieeexplore.ieee.org
The final goal of all industrial machine learning (ML) projects is to develop ML products and
rapidly bring them into production. However, it is highly challenging to automate and …
rapidly bring them into production. However, it is highly challenging to automate and …
Machine learning-assisted approaches in modernized plant breeding programs
In the face of a growing global population, plant breeding is being used as a sustainable tool
for increasing food security. A wide range of high-throughput omics technologies have been …
for increasing food security. A wide range of high-throughput omics technologies have been …
Towards CRISP-ML (Q): a machine learning process model with quality assurance methodology
S Studer, TB Bui, C Drescher, A Hanuschkin… - Machine learning and …, 2021 - mdpi.com
Machine learning is an established and frequently used technique in industry and
academia, but a standard process model to improve success and efficiency of machine …
academia, but a standard process model to improve success and efficiency of machine …
[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 …
Data set quality in machine learning: consistency measure based on group decision making
Abstract Performance of Machine Learning models heavily depends on the quality of the
training dataset. Among others, the quality of training data relies on the consistency of the …
training dataset. Among others, the quality of training data relies on the consistency of the …
In-database machine learning with SQL on GPUs
In machine learning, continuously retraining a model guarantees accurate predictions based
on the latest data as training input. But to retrieve the latest data from a database, time …
on the latest data as training input. But to retrieve the latest data from a database, time …
Nebulastream: Complex analytics beyond the cloud
The arising Internet of Things (IoT) will require significant changes to current stream
processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present …
processing engines (SPEs) to enable large-scale IoT applications. In this paper, we present …
Recent advances in data engineering for networking
This tutorial paper examines recent advances in data engineering, focusing on aspects of
network management and orchestration. We provide a comprehensive analysis of …
network management and orchestration. We provide a comprehensive analysis of …
Materialization and reuse optimizations for production data science pipelines
Many companies and businesses train and deploy machine learning (ML) pipelines to
answer prediction queries. In many applications, new training data continuously becomes …
answer prediction queries. In many applications, new training data continuously becomes …
Recursive SQL and GPU-support for in-database machine learning
In machine learning, continuously retraining a model guarantees accurate predictions based
on the latest data as training input. But to retrieve the latest data from a database, time …
on the latest data as training input. But to retrieve the latest data from a database, time …