With the increasing volume of astronomical data generated by modern survey
telescopes, automated pipelines and machine learning techniques have become
crucial for analyzing and extracting knowledge from these datasets. Anomaly
detection, i.e. the task of identifying irregular or unexpected patterns in the
data, is a complex challenge in astronomy. In this paper, we propose
Multi-Class Deep Support Vector Data Description (MCDSVDD), an extension of the
state-of-the-art anomaly detection algorithm One-Class Deep SVDD, specifically
designed to handle different inlier categories with distinct data
distributions. MCDSVDD uses a neural network to map the data into hyperspheres,
where each hypersphere represents a specific inlier category. The distance of
each sample from the centers of these hyperspheres determines the anomaly
score. We evaluate the effectiveness of MCDSVDD by comparing its performance
with several anomaly detection algorithms on a large dataset of astronomical
light-curves obtained from the Zwicky Transient Facility. Our results
demonstrate the efficacy of MCDSVDD in detecting anomalous sources while
leveraging the presence of different inlier categories. The code and the data
needed to reproduce our results are publicly available at
this https URL

使用多类深度支持向量数据描述算法 (MCDSVDD) 在天文学中进行异常检测，并通过与其他异常检测算法在大型天文曲线数据集上的性能比较验证了其有效性。

多类别深度支持向量聚类：天文学中基于不同内群类别的异常检测方法

Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with  Distinct Inlier Categories

Machine Learning Operations (MLOps) is becoming a highly crucial part of
businesses looking to capitalize on the benefits of AI and ML models. This
research presents a detailed review of MLOps, its benefits, difficulties,
evolutions, and important underlying technologies such as MLOps frameworks,
Docker, GitHub actions, and Kubernetes. The MLOps workflow, which includes
model design, deployment, and operations, is explained in detail along with the
various tools necessary for both model and data exploration and deployment.
This article also puts light on the end-to-end production of ML projects using
various maturity levels of automated pipelines, with the least at no automation
at all and the highest with complete CI/CD and CT capabilities. Furthermore, a
detailed example of an enterprise-level MLOps project for an object detection
service is used to explain the workflow of the technology in a real-world
scenario. For this purpose, a web application hosting a pre-trained model from
TensorFlow 2 Model Zoo is packaged and deployed to the internet making sure
that the system is scalable, reliable, and optimized for deployment at an
enterprise level.

该研究详细介绍了 MLOps，它的好处，困难，演变以及重要的底层技术，如 MLOps 框架，Docker，GitHub 动作和 Kubernetes。该文章还侧重于使用各种成熟度水平的自动化管道的 ML 项目的端到端生产。它提供了一个企业级 MLOps 项目的详细示例，以解释技术在实际场景中的工作流程。