  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.
