Build business-critical machine learning models at scale using ML Core BKOAI and Azure Machine Learning Microsoft, DATABRICKS
Build TodayBKO AI’s ML Core and Microsoft’s AML together provide a platform to develop and deploy ML models quickly and easily at scale and manage and govern them efficiently thereby accelerating the time to ROI.
A development program supporting Digital Twins
and Machine Learning.
Taking data from any source and writing it to and from any appropriate analytics workbench for user quality control, ML Core then routes it to MS Azure ML (AML) where the appropriate ML model to solve the problem is auto selected, and the results passed back to the workbench for user review. e.g. SeeQ, PI, Databricks.
ML Core then uses managed endpoints in Azure ML to deploy and operationalize model deployment, model scoring, logging of metrics, and enabling safe and audited model rollouts at scale.
Automated Machine Learning (Auto ML) combined with ML Core automates the time-consuming and error prone iterative tasks of machine learning model development and builds the pipelines for the data needed for classification, regression, and forecasting. Its goals include pre-processing the input dataset, identification of the models to use, and tuning the hyper-parameters of the selected model.
