Software Improvement Group interviewt Software Engineers van Kepler Vision Technologies

Artificiële Intelligentie (AI) of Kunstmatige Intelligentie projecten zijn, in de kern, software projecten. De Software Improvement Group (SIG) heeft een aantal best practices voor het ontwerp en de uitrol van succesvolle AI projecten geïdentificeerd. Machiel Bruntink en Rob van der Veer gingen om de tafel met Per John en Rob van der Leek van het development team van Kepler Vision Technologies om te leren hoe deze best practices worden toegepast bij AI voor de ouderenzorg. Het artikel gaat over AI engineering, data engineering, data opslag, model engineering en operations. Het begin van het (helaas alleen in het engels beschikbare) artikel leest u hieronder. Voor het oorspronkelijke artikel, klik hier.

AI technology is becoming mainstream, quickly moving beyond the thriving AI startup ecosystem. Even traditional organizations, such as banks, insurance companies, and manufacturers are starting to implement AI as part of their core software. According to Gartner, 46% of organizations are actively experimenting with AI and planning new such initiatives, and 14% have already implemented it. Often, they want to integrate with their existing (BI) data infrastructure and leverage their available software expertise as much as possible. Their management often worries whether the right practices are in place to ensure trustworthy outcomes that will outperform their existing solutions. How can they know if their AI teams are applying current best practices and heading in the right direction? Do they have the right software expertise to apply engineering best practices? Do their processes adequately deal with fundamental challenges such as explainability and fairness?

At SIG, our mission is to help clients improve the health of their software. Clients sometimes struggle with aging legacy software full of outdated design and technology, or they wonder how to prevent the same mistakes when starting anew; or they want to benefit from experimental technology, such as AI, but would prefer to have some assurance. We typically apply a spectrum of instruments to drive our consulting at each particular client; including automatic software quality measurement, open-source (library) health scans, architecture & security analyses, and assessments of development practices.

While we’ve recently updated most of our instruments to consider AI directly, in this article, we focus on AI engineering practices.

Recently, various sources have begun describing best practices for (parts of) the AI engineering process, such as Microsoft and Google, followed by academic research activity, such as the week-long retreat, “SE4ML – Software Engineering for AI-ML-based Systems” in 2020 at SchlossDaghstul, and the catalog of software engineering practices for machine learning (SE-ML) initiative by Leiden University in the Netherlands. To kick start our efforts to build an AI Practices Assessment (AIPA) instrument, we combined such sources with the experiences collected by our own AI practice group at SIG.

In this article, my colleague, Rob van der Veer, and I discuss AI engineering practices and relevant tips and tricks with the team at Kepler Vision Technologies, an Amsterdam-based company providing computer vision and deep learning technology for the health care industry. Its flagship product, the Kepler Night Nurse, assists caregivers by identifying relevant events in video streams, such as an elderly person struggling to get out of bed or falling down.

Kepler Vision Technologies is at the forefront of engineering deep learning pipelines, staying firmly rooted in modern-day software development practices. Their software engineers, Per John and Rob van der Leek have long been active in the software quality assurance business as both engineers and consultants. Their expertise in software engineering has allowed the company to automate most steps of the AI process, leading to advantages in repeatability, error correction, and speed. In his 2019 blog for Towards Data Science, John outlined their key software development practices for deep learning.

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