# Systems Engineering Published articles for Systems Engineering. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine-Tuning, and On-Device Optimizations DevFeed: [Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine-Tuning, and On-Device Optimizations]() Original publisher: [Read original article]() Author: Gaetan Bahl; Enzo Ruedas; Tess Boivin Published: 2026-03-05T14:16:49Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Robotics](), [Fine-tuning](), [multimodal](), [Artificial Intelligence](), [datasets](), [Latency](), [systems](), [AI Inference](), [SOC](), [data](), [Hardware](), [smolvla]() Tags: [ai](), [best-practices](), [compression](), [compute](), [data](), [datasets](), [embedded](), [engineering](), [fine-tuning](), [hardware](), [inference](), [latency](), [models](), [on-device](), [performance](), [real-time](), [robotics](), [smolvla](), [soc](), [systems-engineering]() ### AI overview A practical guide to deploying Vision-Language-Action models on embedded robotic platforms. It covers reliable dataset recording, fine-tuning ACT and SmolVLA policies, asynchronous inference, latency-aware scheduling, and real-time optimization on the NXP i.MX 95 SoC. ### Source excerpt Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine-Tuning, and On-Device Optimizations Authors: Enzo Ruedas, Tess Boivin Recent advances in Large Language Models have enabled the transition from text-only reasoning to multimodal systems. First, with the integration of visual perception in Vision-Language Models (VLMs), and more recently with the generation of robot actions in Vision-Language-Action (VLA) models. ## Multi-Agent System Reliability DevFeed: [Multi-Agent System Reliability]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2026-02-19T20:41:37Z Content type: tutorial Language: en Sources: [Alex Ewerlof Notes]() Topics: [Multi Agent Systems](), [LLMs](), [Artificial Intelligence](), [systems]() Tags: [architecture](), [llms](), [multi-agent](), [parallel](), [reliability-engineering](), [systems-engineering]() ### AI overview This article presents four architecture patterns--hierarchy, consensus, adversarial debate, and knock-out--to improve the reliability of multi-agent systems. It explains that underlying LLMs remain prone to hallucination, logical fallacies, and context drift, and that multi-agent architectures can amplify these errors and make debugging harder. ### Source excerpt 4 patterns to tame multi-agent systems for reliability ## Emergent properties DevFeed: [Emergent properties]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2025-12-05T13:23:18Z Content type: tutorial Language: en Sources: [Alex Ewerlof Notes]() Topics: [Resilience](), [systems](), [Latency]() Tags: [api](), [database](), [latency](), [leadership](), [resilience](), [systems](), [systems-engineering](), [technical]() ### AI overview This tutorial explains emergent properties in systems engineering, distinguishing nominal, weak, and strong emergence from resultant properties. It discusses how interactions and dependencies between system parts affect reliability, maintainability, predictability, and cost, with examples for technical leadership and engineering. ### Source excerpt What are nominal, weak and strong emergent properties and how to identify and mitigate their negative impact in system design? ## AI Systems Engineering Patterns DevFeed: [AI Systems Engineering Patterns]() Original publisher: [Read original article]() Author: Alex Ewerlöf Published: 2025-11-30T11:56:00Z Content type: article Language: en Sources: [Alex Ewerlof Notes]() Topics: [Artificial Intelligence](), [AI Engineering](), [systems]() Tags: [ai](), [ai-engineering](), [engineering](), [patterns](), [systems-engineering](), [techniques]() ### AI overview The article presents 30 techniques from conventional systems engineering that can be applied to AI engineering. ### Source excerpt 30 techniques from conventional system engineering to supercharge AI Engineering ## MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering DevFeed: [MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-11-19T17:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [AI, ML & Data Engineering](), [Training AI Models]() Tags: [ai](), [community](), [data-engineering](), [deployment](), [hardware](), [inference](), [infrastructure](), [large-language-models-llms](), [machine-learning](), [model-development](), [monitoring-maintenance](), [optimization](), [socratiq](), [systems-engineering](), [tensorflow]() ### AI overview An overview of MLSysBook.ai's machine-learning systems engineering concepts, connected to the TensorFlow ecosystem. It emphasizes the infrastructure, hardware, scaling, deployment, efficiency, and reliability considerations required to build and operate machine-learning systems. ### Source excerpt Posted by Jason Jabbour, Kai Kleinbard and Vijay Janapa Reddi (Harvard University) Everyone wants to do the modeling work, but no one wants to do the engineering. If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing and building the engines that take them there. Introduction "Everyone wants to do modeling, but no one wants to do the engineering," highlights a stark reality in the machine learning (ML) world: the allure of building sophisticated models often overshadows the critical task of engineering them into robust, scalable, and efficient systems. The reality is that ML and systems are inextricably linked. Models, no matter how innovative, are computationally demanding and require substantial resources--with the rise of generative AI and increasingly complex models, understanding how ML infrastructure scales becomes even more critical. Ignoring the system's limitations during model development is a recipe for disaster. Unfortunately, educational resources on the systems side of machine learning are lacking. There are plenty of textbooks and materials on deep learning theory and concepts. However, we truly need more resources on the infrastructure and systems side of machine learning. Critical questions--such as how to optimize models for specific hardware, deploy them at scale, and ensure system efficiency and reliability--are still not adequately understood by ML practitioners. This lack of understanding is not due to disinterest but rather a gap in available knowledge. One significant resource addressing this gap is MLSysBook.ai. This blog post explores key ML systems engineering concepts from MLSysBook.ai and maps them to the TensorFlow ecosystem to provide practical insights for building efficient ML systems. The Connection Between Machine Learning and Systems Many think machine learning is solely about extracting patterns and insights from data. While this is fundamental, it's only part of the s ## Two Perspectives on the End-to-End Principle DevFeed: [Two Perspectives on the End-to-End Principle]() Original publisher: [Read original article]() Author: Nelson Elhage Published: 2017-06-11T20:42:01Z Content type: opinion Language: en Sources: [Nelson Elhage]() Topics: [systems](), [Architecture & Design](), [Complex Systems](), [Software Engineering]() Tags: [argument](), [complex-systems](), [perspectives](), [software-design](), [systems-engineering]() ### AI overview The article reflects on the end-to-end principle as a general systems-design heuristic. It explains that functions such as correctness may be better handled at the ends of a system rather than at every lower-level interface, and questions whether complex systems can be made correct simply by composing correct subsystems. ### Source excerpt Back when I was an undergraduate, as part of a class called "Computer Systems Engineering", we read numerous classic papers of systems design. I enjoyed and learned a great deal from many of these papers, but one that paper that has stuck with me in particular was Saltzer et al's "End-to-End Arguments in Systems Design". The paper is a very general tract on systems design - it does explore several examples of concrete systems or applications, but it ultimately expounds upon the end-to-end principle as a perspective or design heuristic that can apply to virtually any system design. ## Open Sourcing Daytona: A Framework For Automated and Application-agnostic Performance Analysis DevFeed: [Open Sourcing Daytona: A Framework For Automated and Application-agnostic Performance Analysis]() Original publisher: [Read original article]() Author: mikesefanov Published: 2017-05-23T16:00:06Z Content type: release Language: en Sources: [Yahoo]() Topics: [Testing](), [benchmarking](), [Framework](), [Open Source](), [Tool](), [ui](), [Command-line interface](), [CI/CD](), [Cloud](), [on-prem]() Tags: [architecture](), [ci-cd](), [cloud](), [cloud-services](), [command-line](), [data](), [framework](), [metrics](), [on-prem](), [open-source](), [performance](), [performance-analysis](), [performance-testing](), [systems-engineering](), [tool](), [ui](), [yahoo](), [yahoo-engineering]() ### AI overview Yahoo presents Daytona as an open-source, application-agnostic framework for automated performance testing and analysis. It provides repeatable test execution, standardized reporting, profiling, a unified interface, command-line script support, scheduling, CI/CD integration, and deployment on-premises or in the cloud. ### Source excerpt By Sapan Panigrahi and Deepesh Mittal Today, we are pleased to offer Daytona, an open-source framework for automated performance testing and analysis, to the community. Daytona is an application-agnostic framework to conduct integrated performance testing and analysis with repeatable test execution, standardized reporting, and built-in profiling support. Daytona gives you the capability to build a customized test harness in a single, unified framework to test and analyze the performance of any application. You'll get easy repeatability, consistent reporting, and the ability to capture trends. Daytona's UI accepts a performance testing script that can run on a command line. This includes websites, databases, networks, or any workload you need to test and tune for performance. You can submit tests to the scheduler queue from the Daytona UI or from your CI/CD tool. You can deploy Daytona as a hosted service in your on-prem environment or on the public cloud of your choice. In fact, you can even host test harnesses for multiple applications with a single centralized service so that developers, architects, and systems engineers from different parts of your organization can work together on a unified view and manage your performance analysis on a continuous basis. Daytona's differentiation lies in its ability to aggregate and present essential aspects of application, system, and hardware performance metrics with a simple and unified user interface. This helps you maintain your focus on performance analysis without changing context across various sources and formats of data. The overall goal of performance analysis is to find ways of maximizing application throughput with minimum hardware resource and the best user experience. Metrics and insights from Daytona help achieve this objective. Prior to Daytona, we created multiple, heterogenous performance tools to meet the specific needs of various applications. This meant that we often stored test results inconsistently, maki