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  • Machine learning operations - Azure Architecture Center
    Learn about a single deployable set of repeatable and maintainable patterns for creating machine learning CI CD and retraining pipelines
  • Register and work with models - Azure Machine Learning | Microsoft Learn
    In this article, you learn to register and work with models in Azure Machine Learning by using: The Azure Machine Learning studio UI The Azure Machine Learning V2 CLI The Python Azure Machine Learning V2 SDK You learn how to: Create registered models in the model registry from local files, datastores, or job outputs
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    Learn about monitoring the performance of models deployed to production on Azure Machine Learning, including lookback windows, monitoring signals, and metrics
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    Use automated machine learning with multiple Microsoft products for faster insights regardless of machine learning skill level Understand models better Better understand models and compare model performance using the built-in support for experiment run summaries and detailed metrics visualizations
  • Azure AI in Business Central – The Forecasting Model
    The Forecasting Model for Microsoft Dynamics 365 Business Central lets you analyze data in historical periods to make predictions about cash flow and inventory levels This model uses the Execute R Script module to run the R scripts that calculate the forecast and determine its accuracy
  • Cohere Models Now Available on Managed Compute in Azure AI Foundry . . .
    Over the course of the last year, we have launched several Cohere models on Azure as Serverless Standard (pay-go) offering We’re excited to announce that Cohere's latest models—Command A, Rerank 3 5, and Embed 4—are now available on Azure AI Foundry models via Managed Compute This launch allows enterprises and developers to now deploy Cohere models instantly with their own Azure quota
  • Explore data and train models | Microsoft Press Store
    You decide to use Azure Machine Learning’s Automated Machine Learning (AutoML) feature to quickly build and deploy the model We’ll break this down into setting up your environment, preparing your tabular data, and then using AutoML on your tabular data by looking at a real-world scenario


















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