Overview

Organisations with existing data science and machine learning teams often work in environments that are inefficient, do not promote collaboration, and therefore do not maximise the potential value from this scarce skill set. DiUS helps organisations reach a greater level of productivity and efficiency by integrating MLOps into their machine learning workflows and modernising their practices.

Opportunities

DiUS can help automate, accelerate and manage machine learning (ML) workflows so that teams can focus on delivering business value rather than managing infrastructure and platforms. We do this by helping organisations:

• Migrate existing ML pipelines to a fully managed, high availability service.
• Speed-up model development and deployment with repeatable ML pipelines.
• Incorporate human-in-the loop workflows for more efficient data collection and labeling.
• Manage different stages of the ML pipeline such as data labeling, feature engineering, model creation, and deployment.
• Monitor models in production, detect performance degradation or data and model drift, and automate model retraining.