Managing your ML lifecycle with Azure Databricks and Azure Machine Learning | OD210
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Machine learning development has new
Machine learning development has new complexities beyond software development. There are a myriad of tools and frameworks which make it hard to track experiments, reproduce results, and deploy machine learning models. Learn how you can accelerate, collaborate and manage your end-to-end machine learning lifecycle on Azure Databricks using MLflow and Azure ML to reliably build, share, and deploy machine learning applications using Azure Databricks.
Deploy models from Azure Databricks to Azure Machine Learning -- https://docs.microsoft.com/en-us/azur...
eBook: Standardizing the machine learning lifecycle -- https://azure.microsoft.com/en-us/res...
Azure Databricks Documentation -- https://docs.microsoft.com/en-us/azur...
Azure Machine Learning Documentation -- https://docs.microsoft.com/en-us/azur...
MLflow Documentation -- https://docs.microsoft.com/en-us/azur...
#MSIgnite
Deploy models from Azure Databricks to Azure Machine Learning -- https://docs.microsoft.com/en-us/azur...
eBook: Standardizing the machine learning lifecycle -- https://azure.microsoft.com/en-us/res...
Azure Databricks Documentation -- https://docs.microsoft.com/en-us/azur...
Azure Machine Learning Documentation -- https://docs.microsoft.com/en-us/azur...
MLflow Documentation -- https://docs.microsoft.com/en-us/azur...
#MSIgnite
4 سال پیش
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