Our ML algorithms are fine, but good results do require a significant team of data specialists, data engineers, field experts, and more support staff. And while the number and cost of expert staff is not constraining enough, our understanding of how to optimize for nodes, layers, and hyperparameters is still primitive. Finally, moving models into production and keeping them up to date is a final hurdle, given that the estimation created by a model can often only be achieved by continuing to use the same expensive and complex architecture used for learning. It should be understood that moving to production is a process and not a step and it starts long before the model development. Its first step is to define the business objective, the hypothesis of the value that can be extracted from the data, and the business ideas for its application.
MLOps — is a combination of technologies and processes of machine learning and approaches to the implementation of developed models in business processes. The very concept emerged as an analogy of DevOps in relation to ML models and ML approaches. DevOps is an approach to software development that allows increasing the speed of implementation of individual changes while maintaining flexibility and reliability through a number of approaches, including continuous development, division of functions into a number of independent microservices, automated testing and deploying of individual changes, global performance monitoring, a system of prompt response to detected failures, etc.
MLOps, or DevOps for machine learning, allows data science and IT teams to collaborate and accelerate model development and implementation by monitoring, validating, and managing machine learning models.
Of course, there is nothing new here — everyone has been doing it one way or another for a while. Now just a hype word appears behind which there are usually ready-made solutions like Seldon, Kubeflow, or MLflow.