Tootfinder

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
Raj Patel, Himanshu Tripathi, Jasper Stone, Noorbakhsh Amiri Golilarz, Sudip Mittal, Shahram Rahimi, Vini Chaudhary
https://arxiv.org/abs/2506.02032

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations (MLOps) has emerged as an integrative approach addressing these requirements by unifying relevant roles and streamlining ML workflows. As the MLOps market continues to grow, securing these pipelines has become increasingly critical. However, the unified nature of MLOps ecosystem …

MLOps with Microservices: A Case Study on the Maritime Domain
Renato Cordeiro Ferreira (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Rowanne Trapmann (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University), Willem-Jan van den Heuvel (Jheronimus Academy of Data Science, Technical University of Eindhoven, Tilburg University)

MLOps with Microservices: A Case Study on the Maritime Domain
This case study describes challenges and lessons learned on building Ocean Guard: a Machine Learning-Enabled System (MLES) for anomaly detection in the maritime domain. First, the paper presents the system's specification, and architecture. Ocean Guard was designed with a microservices' architecture to enable multiple teams to work on the project in parallel. Then, the paper discusses how the developers adapted contract-based design to MLOps for achieving that goal. As a MLES, Ocean Guard emplo…

Non-Intrusive MLOps-Driven Performance Intelligence in Software Data Planes
Qiong Liu, Jianke Lin, Tianzhu Zhang, Leonardo Linguaglossa
https://arxiv.org/abs/2506.17658

Non-Intrusive MLOps-Driven Performance Intelligence in Software Data Planes
The last decade has witnessed the proliferation of network function virtualization (NFV) in the telco industry, thanks to its unparalleled flexibility, scalability, and cost-effectiveness. However, as the NFV infrastructure is shared by virtual network functions (VNFs), sporadic resource contentions are inevitable. Such contention makes it extremely challenging to guarantee the performance of the provisioned network services, especially in high-speed regimes (e.g., Gigabit Ethernet). Existing s…

AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
Satoshi Tanaka, Samrat Thapa, Kok Seang Tan, Amadeusz Szymko, Lobos Kenzo, Koji Minoda, Shintaro Tomie, Kotaro Uetake, Guolong Zhang, Isamu Yamashita, Takamasa Horibe
https://arxiv.org/abs/2506.00645

AWML: An Open-Source ML-based Robotics Perception Framework to Deploy for ROS-based Autonomous Driving Software
In recent years, machine learning technologies have played an important role in robotics, particularly in the development of autonomous robots and self-driving vehicles. As the industry matures, robotics frameworks like ROS 2 have been developed and provides a broad range of applications from research to production. In this work, we introduce AWML, a framework designed to support MLOps for robotics. AWML provides a machine learning infrastructure for autonomous driving, supporting not only the …