DevOps solutions emerged as a set of practices and solutions that combines development-oriented activities (Dev) with IT operations (Ops) in order to accelerate the development cycle while maintaining efficiency in delivery and predictable, high levels of quality.
The core principles of DevOps include an Agile approach to software development, with iterative, continuous, and collaborative cycles, combined with automation and self-service concepts.
However, the DevOps approach to machine learning (ML) and AI are limited by the fact that machine learning models differ from traditional application development in many ways.
However, DevOps approaches to machine learning (ML) and AI are limited by the fact that machine learning models differ from traditional application development in many ways. For one, ML models are highly dependent on data: training data, test data, validation data, and of course, the real-world data used in inferencing. Simply building a model and pushing it to operation is not sufficient to guarantee performance. DevOps approaches for ML also treat models as “code” which makes them somewhat blind to issues that are strictly data-based, in particular the management of training data, the need for re-training of models, and concerns of model transparency and explainability.