The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
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The shortage for ICT personal in the EU is large and expected to increase. The aim of this research is to contribute to a better understanding of the roles and competences needed, so that education curricula can be better aligned to evolving market demand by answering the research question: Which competence gaps do we need to bridge in order to meet the future need for sufficiently qualified personnel in the EU Software sector? In this research, a mixed method approach was executed in twelve European countries, to map the current and future needs for competences in the EU. The analyses shows changes in demand regarding technical skills, e.g. low-code and a stronger focus on soft skills like communication and critical thinking. Besides this, the research showed educational institutes would do well to develop their curricula in a practical way by integration of real live cases and work together with organizations.
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In my previous post on AI engineering I defined the concepts involved in this new discipline and explained that with the current state of the practice, AI engineers could also be named machine learning (ML) engineers. In this post I would like to 1) define our view on the profession of applied AI engineer and 2) present the toolbox of an AI engineer with tools, methods and techniques to defy the challenges AI engineers typically face. I end this post with a short overview of related work and future directions. Attached to it is an extensive list of references and additional reading material.
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Over the past three years we have built a practice-oriented, bachelor level, educational programme for software engineers to specialize as AI engineers. The experience with this programme and the practical assignments our students execute in industry has given us valuable insights on the profession of AI engineer. In this paper we discuss our programme and the lessons learned for industry and research.
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Recently, the job market for Artificial Intelligence (AI) engineers has exploded. Since the role of AI engineer is relatively new, limited research has been done on the requirements as set by the industry. Moreover, the definition of an AI engineer is less established than for a data scientist or a software engineer. In this study we explore, based on job ads, the requirements from the job market for the position of AI engineer in The Netherlands. We retrieved job ad data between April 2018 and April 2021 from a large job ad database, Jobfeed from TextKernel. The job ads were selected with a process similar to the selection of primary studies in a literature review. We characterize the 367 resulting job ads based on meta-data such as publication date, industry/sector, educational background and job titles. To answer our research questions we have further coded 125 job ads manually. The job tasks of AI engineers are concentrated in five categories: business understanding, data engineering, modeling, software development and operations engineering. Companies ask for AI engineers with different profiles: 1) data science engineer with focus on modeling, 2) AI software engineer with focus on software development , 3) generalist AI engineer with focus on both models and software. Furthermore, we present the tools and technologies mentioned in the selected job ads, and the soft skills. Our research helps to understand the expectations companies have for professionals building AI-enabled systems. Understanding these expectations is crucial both for prospective AI engineers and educational institutions in charge of training those prospective engineers. Our research also helps to better define the profession of AI engineering. We do this by proposing an extended AI engineering life-cycle that includes a business understanding phase.
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The current set of research methods on ictresearchmethods.nl contains only one research method that refers to machine learning: the “Data analytics” method in the “Lab” strategy. This does not reflect the way of working in ML projects, where Data Analytics is not a method to answer one question but the main goal of the project. For ML projects, the Data Analytics method should be divided in several smaller steps, each becoming a method of its own. In other words, we should treat the Data Analytics (or more appropriate ML engineering) process in the same way the software engineering process is treated in the framework. In the remainder of this post I will briefly discuss each of the existing research methods and how they apply to ML projects. The methods are organized by strategy. In the discussion I will give pointers to relevant tools or literature for ML projects.
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The full potential of predictive maintenance has not yet been utilised. Current solutions focus on individual steps of the predictive maintenance cycle and only work for very specific settings. The overarching challenge of predictive maintenance is to leverage these individual building blocks to obtain a framework that supports optimal maintenance and asset management. The PrimaVera project has identified four obstacles to tackle in order to utilise predictive maintenance at its full potential: lack of orchestration and automation of the predictive maintenance workflow, inaccurate or incomplete data and the role of human and organisational factors in data-driven decision support tools. Furthermore, an intuitive generic applicable predictive maintenance process model is presented in this paper to provide a structured way of deploying predictive maintenance solutions https://doi.org/10.3390/app10238348 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
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Many quality aspects of software systems are addressed in the existing literature on software architecture patterns. But the aspect of system administration seems to be a bit overlooked, even though it is an important aspect too. In this work we present three software architecture patterns that, when applied by software architects, support the work of system administrators: PROVIDE AN ADMINISTRATION API, SINGLE FILE LOCATION, and CENTRALIZED SYSTEM LOGGING. PROVIDE AN ADMINISTRATION API should solve problems encountered when trying to automate administration tasks. The SINGLE FILE LOCATION pattern should help system administrators to find the files of an application in one (hierarchical) place. CENTRALIZED SYSTEM LOGGING is useful to prevent coming up with several logging formats and locations. Abstract provided by the authors. Published in PLoP '13: Proceedings of the 20th Conference on Pattern Languages of Programs ACM.
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Due to the changing technological possibilities of services, the demands that society places on the level of service provided by the Dutch Central Government (DCG) are changing rapidly. To accommodate this, the Dutch government is improving its processes in such a way that they become more agile and are continuously improved. However, the DCG struggles with the implementation of improvement tools that can support this. The research described in this paper aims to deliver key factors that influence the adoption of tools that improve the agile way of working and continuous improvement at the DCG. Therefore, a literature review has been conducted, from which 24 factors have been derived. Subsequently, 9 semi structured interviews have been conducted to emphasize the perspective of employees at the DCG. In total, 7 key factors have been derived from the interviews. The interviewees consisted of both employees from departments who already worked with tools to improve agile working and continuous improvement as well as employees from departments who haven’t used such tools yet. An important insight based on this research is that the aims, way of working and scope of the improvement tools must be clear for all the involved co-workers
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In this paper we research the following question: What motivational factors relate, in which degree, to intentions on compliance to ISP and how could these insights be utilized to promote endusers compliance within a given organization? The goal of this research is to provide more insight in the motivational factors applicable to ISP and their influence on end-user behavior, thereby broadening knowledge regarding information systems security behaviors in organizations from the viewpoint of non-malicious abuse and offer a theoretical explanation and empirical support. The outcomes are also useful for practitioners to complement their security training and awareness programs, in the end helping enterprises better effectuate their information security policies. In this study an instrument is developed that can be used in practice to measure an organizational context on the effects of six motivational factors recognized. These applicable motivational factors are determined from literature and subsequently evaluated and refined by subject matter experts. A survey is developed, tested in a pilot, refined and conducted within four organizations. From the statistical analysis, findings are reported and conclusions on the hypothesis are drawn. Recommended Citation Straver, Peter and Ravesteyn, Pascal (2018) "End-users Compliance to the Information Security Policy: A Comparison of Motivational Factors," Communications of the IIMA: Vol. 16 : Iss. 4 , Article 1. Available at: https://scholarworks.lib.csusb.edu/ciima/vol16/iss4/1
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