Today, products are no longer isolated artifacts, but nodes in networked systems. This means that traditional, linearly conceived life cycle models are reaching their limits: Interoperability across disciplines, variant and configuration management, traceability, and governance across organizational boundaries are becoming key factors. This collective contribution classifies the state of the art and proposes a practical frame of reference for SoS lifecycle management, model-based systems engineering (MBSE) as the semantic backbone, product lifecycle management (PLM) as the governance and configuration level, CAD-CAE as model-derived domains, and digital thread and digital twin as continuous feedback. Based on current literature and industry experience, mobility, healthcare, and the public sector, we identify four principles: (1) referenced architecture and data models, (2) end-to-end configuration sovereignty instead of tool silos, (3) curated models with clear review gates, and (4) measurable value contributions along time, quality, cost, and sustainability. A three-step roadmap shows the transition from product- to network- centric development: piloting with reference architecture, scaling across variant and supply chain spaces, organizational anchoring (roles, training, compliance). The results are increased change robustness, shorter throughput times, improved reuse, and informed sustainability decisions. This article is aimed at decision-makers and practitioners who want to make complexity manageable and design SoS value streams to be scalable.

The explosion in artificial intelligence (AI) applications is pushing the development of AI-dedicated data centers (AIDCs), creating management challenges that traditional methods and standalone AI solutions struggle to address. While digital twins are beneficial for AI-based design validation and operational optimization, current AI methods for their creation face limitations. Specifically, physical AI (PhyAI) aims to capture the underlying physical laws, which demands extensive, case-specific customization, and generative AI (GenAI) can produce inaccurate or hallucinated results. We propose Fusion Intelligence, a novel framework synergizing GenAI's automation with PhyAI's domain grounding. In this dual-agent collaboration, GenAI interprets natural language prompts to generate tokenized AIDC digital twins. Subsequently, PhyAI optimizes these generated twins by enforcing physical constraints and assimilating real-time data. Case studies demonstrate the advantages of our framework in automating the creation and validation of AIDC digital twins. These twins deliver predictive analytics to support power usage effectiveness (PUE) optimization in the design stage. With operational data collected, the digital twin accuracy is further improved compared with pure physics-based models developed by human experts. Fusion Intelligence offers a promising pathway to accelerate digital transformation. It enables more reliable and efficient AI-driven digital transformation for a broad range of mission-critical infrastructures.

Complexity is driven in part by 5G itself, which uses a much broader set of frequency bands, can prioritize services based on latency, and supports huge increases in the number of network elements and end-user devices. But there is a plethora of other changes which further increase complexity. These include the evolution from physical hardware to virtual and cloud native networks, end-to-end network slicing, the adoption of Open Radio Access Network (RAN) technologies and the addition of new enterprise business services. There are also multi-technology networks with some communications service providers (CSPs) running 2G, 3G, 4G/LTE and 5G networks in parallel, as well as multi-vendor networks with typically two to four different RAN vendors deployed in the network. Artificial intelligence (AI) and machine learning (ML) are becoming commonplace in the telecoms industry and are often the only way to manage the complexity we see in today's multi-vendor, multi-technology networks.

How many machine learning platforms run on Kubernetes? Which machine learning platforms can run in air-gapped environments? How common are feature stores in current machine learning platforms? There are many commercial and open-source machine learning platforms on the market today. While Gartner's Magic Quadrants and Forrester's Waves can inform, these views onto the marketplace are not neutral: they are based on vendor demonstrations and customer surveys rather than hands-on software evaluations or in-depth studies of available documentation.

The development of artificial intelligence (AI) has made various industries eager to explore the benefits of AI. There is an increasing amount of research surrounding AI, most of which is centred on the development of new AI algorithms and techniques. However, the advent of AI is bringing an increasing set of practical problems related to AI model lifecycle management that need to be investigated. We address this gap by conducting a systematic mapping study on the lifecycle of AI model. Through quantitative research, we provide an overview of the field, identify research opportunities, and provide suggestions for future research. Our study yields 405 publications published from 2005 to 2020, mapped in 5 different main research topics, and 31 sub-topics. We observe that only a minority of publications focus on data management and model production problems, and that more studies should address the AI lifecycle from a holistic perspective.

Across a range of industries, and specifically in the industrial automation vertical, there is broad agreement that the deployment of modern computing resources with cloud native models of software lifecycle management will become ever more pervasive. Placing virtualized computing resources nearer to where multiple streams of data are created is well established. It is the path to address system latency, privacy, cost and resiliency challenges that a pure cloud computing approach cannot address. This paradigm shift was initiated at Cisco Systems around 2010, under the label "fog computing" and progressively morphed into what is now known as "edge computing". The requirements of mission critical industrial systems That said, the full potential of this transformation in both computing and data analytics is far from being realized.

For most professional software developers, using application lifecycle management (ALM) is a given. Data scientists, many of whom do not have a software development background, often have not used lifecycle management for their machine learning models. That's a problem that's much easier to fix now than it was a few years ago, thanks to the advent of "MLops" environments and frameworks that support machine learning lifecycle management. The easy answer to this question would be that machine learning lifecycle management is the same as ALM, but that would also be wrong. That's because the lifecycle of a machine learning model is different from the software development lifecycle (SDLC) in a number of ways.

ModelOps (model operations) is a set of technology processes related to DevOps, but specifically geared to managing the lifecycle for machine learning and artificial intelligence (AI) models. ModelOps coordinates the activities between application and model development. It uses AutoAI and other technologies for model building, in concert with continuous integration and continuous deployment (CI/CD) to update the model on a regular basis. ModelOps defines the cadences between model behavior over time and the processes used to build, evaluate, and consume the models with the application development lifecycle. ModelOps uses the pipeline concept prevalent in computing processes, such as application development and artificial intelligence (AI) model development.

Looking to extend its industry leading security capabilities, Blue Prism announced a partnership with SailPoint, a market leader in enterprise identity management. This partnership gives enterprises added visibility and transparency into governing digital workers, resulting in improved compliance reporting, full automation lifecycle management and better security. The integration of Blue Prism's connected-RPA platform with SailPoint helps organizations maintain and control credentials of digital workers, including those that meet defined Separation of Duties (SoD) policies. By maintaining these credentials and granting access through SailPoint, digital workers can execute systems-based tasks, just as their human counterparts do, securely and at scale. This ability to disable or delete credentials quickly and accurately, while monitoring and auditing access, gives enterprises improved compliance reporting and full lifecycle management and security.

Gluware Automation v3.6 extends the platform API capabilities including integrations with the Mist and Ansible platforms and introduces industry-first lifecycle management and infrastructure integration enhancements. The new version was developed in response to a growing list of global enterprise customers seeking to scale their network automation capabilities to work in their current environment and across more nodes to be faster within their complex networks. Unique in the industry, Gluware is the only solution that offers this level of Intent-Based network automation capabilities without coding or scripting and in support of a tapestry of automation solutions for multi-vendor, brownfield and greenfield networks. For the first time, companies can use the new Intelligent Model Discovery (IMD) Workflow in the Gluware platform to read in an existing device configuration. This puts the power of "infrastructure as code" into the hands of network engineers enabling powerful automation with no IT staff coding required.