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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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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Granular materials (GMs) are simply a collection of individual particles, e.g., rice, coffee, iron-ore. Although straightforward in appearance, GMs are key to several processes in chemical-pharmaceutical, high-tech, agri-food and energy industry. Examples include laser sintering in additive manufacturing, tableting in pharma or just mixing of your favourite crunchy muesli mix in food industry. However, these bulk material handling processes are notorious for their inefficiency and ineffectiveness. Thereby, affecting the overall expenses and product quality. To understand and enhance the quality of a process, GMs industries utilise computer-simulations, much like how cars and aeroplanes have been designed and optimised since the 1990s. Just as how engineers utilise advanced computer-models to develop our fuel-efficient vehicle design, energy-saving granular processes are also developed utilising physics-based simulation-models, using a computer. Although physics-based models can effectively optimise large-scale processes, creating and simulating a fully representative virtual prototype of a GMs process is very iterative, computationally expensive and time intensive. On the contrary, given the available data, this is where machine learning (ML) could be of immense value. Like how ML has transformed the healthcare, energy and other top sectors, recent ML-based developments for GMs show serious promise in faster virtual prototyping and reduced computational cost. Enabling industries to rapidly design and optimise, enhancing real-time data-driven decision making. GranML aims to empower the GMs industries with ML. We will do so by (i) performing an in-depth GMs-ML literature review, (ii) developing open-access ML implementation guidelines; and (iii) an open-source proof-of-concept for an industry-relevant use case. Eventually, our follow-up mission is to build upon this vital knowledge by (i) expanding the consortium; (ii) co-developing a unified methodology for efficient computer-prototyping, unifying physics- and ML-based technologies for GMs; (iii) enhancing the existing computer-modelling infrastructure; and (iv) validating through industry focused demonstrators.
Volgens onderzoek van McKinsey is marketing het vakgebied waar AI de meeste waarde toe gaat voegen, onder andere op het gebied van personalisatie. Hierdoor verandert het stakeholderveld waarin de marketeer personalisatie-algoritmen toepast significant, zo werkt hij steeds vaker samen met data scientists, AI-architecten en data engineers. Dit onderzoek richt zich op de vraag welke handvatten marketeers nodig hebben om tot een verantwoorde personalisatie-praktijk te komen.
With increasing labor shortages, sectors using mobile machines (automotive/industry/agrifood/logistics) have a rising need for productivity improvement. With evolving technology, mobile machine control has stepped from hydraulics to electronics using sensors and smart systems to support drivers and allowing intelligent and automated machine functions. Verification and validation costs of such complex functionality urge the need for virtual solution routes to limit the lead time, cost and safety issues of real-world testing. RAAK-mkb project Fast&Curious developed tools to enable model-driven development for the control of a wide range of vehicle systems. This included automatic code generation support from MATLAB/Simulink® into the Bodas RC30 family vehicle controllers from Bosch Rexroth (see www.openMBD.com). The solution has been adopted by several SMEs allowing them to start working in a model-driven way, helping them to do virtual verification&validation, lowering development time and costs. Meanwhile, Rexroth adopted MATLAB/Simulink for core vehicle functions development and currently develops Fast&Curious-alike automatic code generation support for their recent RC40 controllers. Virtuoso aims to further improve productivity on simulation level by creating an interface layer in Simulink to (automatically) test impact of hardware interface imperfections and failures, such as noise and short circuits, as well as to seamlessly switch between continuous (early development) and discretized (deployment-oriented) input/output behavior. Companies like Emoss and Jautomatisering are interested in such solutions, allowing them to adopt efficient, model-driven processes and supporting their engineers in the required hydraulics-to-software/electronics skill-shift. The solution connects well to future developments like robotization. Besides supporting development of vehicle automation and mobile robotics, MATLAB/Simulink also supports ROS (Robot Operating System) via co-simulation and co-deployment. ROS has become the standard in (mobile) robot control development and is used by many parties. Virtuoso further closes the gap between development and deployment and allows future integration in mobile robotics, foreseen as next step.
