DP-3007: Train and Deploy a Machine Learning Model with Azure Machine Learning (Live Online)

The DP-3007: Train and Deploy a Machine Learning Model with Azure Machine Learning course covers the end-to-end process of building, training, and deploying machine learning models using Azure Machine Learning, including data preparation, model training, and deployment strategies.ObjectivesMake data available in Azure Machine LearningWork with compute targets in Azure Machine LearningWork with environments in Azure Machine LearningRun a training script as a command job in Azure Machine LearningTrack model training with MLflow in jobsRegister an MLflow model in Azure Machine LearningDeploy a model to a managed online endpointTarget AudienceData ScientistAI EngineerCOURSE OUTLINEMake data available in Azure Machine LearningAccess data by using Uniform Resource Identifiers URIsConnect to cloud data sources with datastoresUse data asset to access specific files or foldersLab Make data available in Azure Machine LearningWork with compute targets in Azure Machine LearningChoose the appropriate compute targetWork with compute instances and clustersManage installed packages with environmentsLab Work with compute resourcesWork with environments in Azure Machine LearningUnderstand environments in Azure Machine LearningExplore and use curated environmentsCreate and use custom environmentsLab Work with environmentsRun a training script as a command job in Azure Machine LearningConvert a notebook to a scriptTest scripts in a terminalRun a script as a command jobUse parameters in a command jobLab Run a training script as a command jobTrack model training with MLflow in jobsUse MLflow when you run a script as a jobReview metrics parameters artifacts and models from a runLab Use MLflow to track training jobsRegister an MLflow model in Azure Machine LearningLog models with MLflowUnderstand the MLmodel formatRegister an MLflow model in Azure Machine LearningLab Log and register models with MLflowDeploy a model to a managed online endpointUse managed online endpointsDeploy your MLflow model to a managed online endpointDeploy a custom model to a managed online endpointTest online endpointsLab Deploy an MLflow model to an online endpoint