The last decade, the practices of continuous delivery and deployment have taken the software engineering world by storm. While applications used to be released in an ad hoc manner, breakthroughs in (amongst others) continuous integration, infrastructure-as-code and log monitoring have turned the reliable release of cloud applications into a manageable achievement for most companies. However, the advent of AI models seems to have caused a “reset”, pushing companies to reinvent the way in which they release high-quality products that now not only rely on source code, but also on data and models. This talk will focus on the key ingredients of successful pre-AI release engineering practices, then will connect those to newly emerging, post-AI release engineering practices. After this talk, the audience will understand the major challenges software companies face to release their AI product multiple times a day, as well as the opportunities for predictive models and data analytics.