Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined "Software 2.0," but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.
翻译:通过机器学习获得的人工智能在数字社会中日益被使用。基于机器学习的解决方案带来了巨大的机遇,因此创造了“软件2.0”,但也给工程界带来了巨大的挑战。由于数据科学家在开发机器学习模型时使用的实验方法,灵活性是一个基本特征。在本主旨演讲中,我们讨论了对机器学习发展至关重要的两个当代发展现象,即笔记本界面和MLOPs。首先,我们提出了一个解决方案,通过支持向综合发展环境的简单过渡,可以弥补笔记本工作的某些内在弱点。第二,我们建议通过在MLOPs背景下引入隐喻支持和再栏来加强AI系统的工程。基于机器学习的解决方案在性质上是动态的,我们说,为了保证明天可靠的人工智能系统的质量,需要加强持续工程。