Not long ago, the biggest focus in AI/Machine Learning (ML) was research & development. Since ML has become mainstream in many digital applications, there has been a shift in focus to productionisation of ML models and products.
Business stakeholders invest in ML productionisation as a catalyst for producing real value from ML work. For instance, having a ready to use API to extend ML results into production can be a strategy to realise value and gain traction.
ML CI/CD is one of the best practices to deliver successful ML productionisation. What is it, and why is it important?
It is a set of practices, toolings, systems and platforms to support iterative and continuous development and delivery of ML.
Benefits of establishing CI/CD practice in ML teams include an easier end-to-end path to production, improved quality and productivity, and de-risking key issues earlier.