We propose an end-to-end reusable AI infrastructure that automates the whole life-cycle of AI-driven applications from data ingestion and validation, feature engineering, choice of predictive technique, training, deployment of ML models to a REST endpoint, versioning of deployed models, inference, and evaluation of KPI metrics. The infrastructure leverages Azure Machine Learning and the big data platform Cosmos.
Status
Deployed WW to optimize backup scheduling for PostgreSQL and MySQL servers
Impact
- A reusable AI infrastructure reduces the engineering effort to incorporate AI capabilities into cloud applications from months to weeks.
- Our solution avoids backup collisions with peaks of customer activity and therefore achieves several hundred hours of improved customer experience per month in all Azure regions.
