Azure MLOps Prepackaged pipeline : 4-weeks implementation

The MLOps pre-packaged pipeline solution, delivered by Orange Business Services, allows its customers to automate the retraining and the deployment of their Machine Learning (ML) models whilst guaranteeing test coverage, continuous integration, and model monitoring.

MLOps pipeline in a nutshell: • Reduced time to go live: this asset-based service reduces the time necessary to implement a MLOps pipeline by up to 60% comparing to building it from scratch • Ready to use MLOps setup: operative technical asset with MLOps pipeline to automate & deploy every new Machine Learning and Artificial Intelligence solution • Increased MLOps quality: tested solution to reduce the number of errors encountered during implementation, increasing build quality as well

The Azure platform offers a great number of tools and components to build production ready MLOps pipelines. To unlock the power of these cloud native components a skilled team of experts is required. Orange Business Services is providing the expertise as well as the asset of a ready-to-deploy MLOps setup. Orange Business Services’ Azure certified experts will implement and customize its MLOps pipeline solution to bring its customers successfully to the end of their Machine Learning journey.

Orange Business Services’ solution answers the challenges of automating the retraining and the deployment of Machine Learning and Artificial Intelligence models. The most common difficulties raised up when scaling AI and ML are:

• Lack of automation in the testing, the training, and the deployment of Machine Learning solutions • Complexity and required time to move AI solutions from a Proof of Concept to a reliable and in-production solutions • Manual updates or retraining of the AI and ML solutions, platforms, and models

The MLOps pre-packaged pipeline Orange Business Services offers answer all these challenges. To facilitate the deployment of such a service and to prove the business value it can generate, the solution has been splitted into two main deployment steps which are called offers.