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Execute Azure Machine Learning service pipelines in Azure Data Factory pipelines Azure updates Microsoft Azure

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You now have the ability to run your Azure Machine Learning service pipelines as a step in your Azure Data Factory pipelines. This allows you to run your machine learning models with data from multiple sources (more than 85 data connectors supported in Data Factory). The seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns. Get started quickly by creating an AzureMLService connection and AzureMLExecutePipelne activity to invoke your Azure Machine Learning pipelines in a Data Factory data pipeline.


New features for Azure Machine Learning are now available Azure updates Microsoft Azure

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Model Interpretability - Machine learning interpretability allows data scientists to explain machine learning models globally on all data, or locally on a specific data point using the state-of-art technologies in an easy-to-use and scalable fashion. Machine Learning interpretability incorporates technologies developed by Microsoft and proven third-party libraries (for example, SHAP and LIME). The SDK creates a common API across the integrated libraries and integrates Azure Machine Learning services. Using this SDK, you can explain machine learning models globally on all data, or locally on a specific data point using the state-of-art technologies in an easy-to-use and scalable fashion. Forecasting via AutomatedML, Automated ML advancements and AutomatedML supported on Databricks, CosmosDB & HDInsight – Automated ML automates parts of the ML workflow, reducing the time it takes to build ML models, freeing data scientists to focus on their important work, while simplifying ML and opening it up to a wider audience.


New automated machine learning capabilities in Azure Machine Learning service Blog Microsoft Azure

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This will enable more people in your organization to leverage machine learning and most importantly allow domain experts to rapidly prototype ML solutions and validate their hypothesis before involving data scientists. If you are an experienced data scientist, automated ML will let you improve productivity and save time by eliminating the need to manually perform the tedious and repetitive tasks of feature engineering, algorithm selection and hyperparameter tuning. You can even start by generating a model with automated ML as a starting point and tune it further. Organizations can also use automated ML to benchmark their models. Many Fortune 500 customers are benefiting from using automated ML. These include a global oil & refinery enterprise that's using automated ML to forecast reservoir production and a medical devices company that's using automated ML for predictive maintenance. Automated ML also powers Microsoft Power BI's AI capabilities, where business analysts can build machine learning models without writing a single line of code. Azure Machine Learning service's automated ML capability is based on a breakthrough from our Microsoft Research division and different from competing solutions in the market. The approach combines ideas from collaborative filtering and Bayesian optimization to search an enormous space of possible machine learning pipelines intelligently and efficiently.


Automated machine learning and MLOps with Azure Machine Learning Blog Microsoft Azure

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Azure Machine Learning is the center for all things machine learning on Azure, be it creating new models, deploying models, managing a model repository, or automating the entire CI/CD pipeline for machine learning. We recently made some amazing announcements on Azure Machine Learning, and in this post, I'm taking a closer look at two of the most compelling capabilities that your business should consider while choosing the machine learning platform. Before we get to the capabilities, let's get to know the basics of Azure Machine Learning. Azure Machine Learning is a managed collection of cloud services, relevant to machine learning, offered in the form of a workspace and a software development kit (SDK). Let us introduce you to Machine Learning with the help of this video where Chris Lauren from the Azure Machine Learning team showcases and demonstrates it.


Automated machine learning and MLOps with Azure Machine Learning Blog Microsoft Azure

#artificialintelligence

Azure Machine Learning is the center for all things machine learning on Azure, be it creating new models, deploying models, managing a model repository, or automating the entire CI/CD pipeline for machine learning. We recently made some amazing announcements on Azure Machine Learning, and in this post, I'm taking a closer look at two of the most compelling capabilities that your business should consider while choosing the machine learning platform. Before we get to the capabilities, let's get to know the basics of Azure Machine Learning. Azure Machine Learning is a managed collection of cloud services, relevant to machine learning, offered in the form of a workspace and a software development kit (SDK). Let us introduce you to Machine Learning with the help of this video where Chris Lauren from the Azure Machine Learning team showcases and demonstrates it.