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What Is Federated Learning?

Standard machine learning approaches require centralizing the training data on one machine or in a datacenter. And Google has built one of the most secure and robust cloud infrastructures for processing this data to make our services better. Now for models trained from user interaction with mobile devices, we’re introducing an additional approach: Federated Learning. Federated Learning enables mobile phones to collaboratively learn a shared prediction model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. This goes beyond the use of local models that … Continue reading What Is Federated Learning?

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Predict prices using regression with ML.NET

You can follow along or download the source code here. Create a console application Create a .NET Core Console Application called “TaxiFarePrediction”. Create a directory named Data in your project to store the data set and model files. Install the Microsoft.ML NuGet Package:In Solution Explorer, right-click the … Continue reading Predict prices using regression with ML.NET

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Create an IoT hub using the Azure portal

Create an IoT hub This section describes how to create an IoT hub using the Azure portal. Sign in to the Azure portal. From the Azure homepage, select the + Create a resource button, and then enter IoT Hub in the Search the Marketplace field. Select IoT Hub from the search results, and then select Create. On the Basics tab, complete the fields as follows: Subscription: Select the subscription to use for your hub. Resource Group: Select a resource group or create a new one. To create a new one, select Create new and fill in the name you want to use. To use an existing resource group, select that resource group. For more information, … Continue reading Create an IoT hub using the Azure portal

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Deploy and run an IoT device simulation in Azure

Deploy Device Simulation When you deploy Device Simulation to your Azure subscription, you must set some configuration options. Sign in to azureiotsolutions.com using your Azure account credentials. Click the Device Simulation tile: Click Try now on the Device Simulation description page: On the Create Device Simulation solution page, enter a unique Solution name. Select the Subscription and Region you want to use to deploy the solution accelerator. Typically, you choose the region closest to you. You must be a global administrator or user in the subscription. Check the box to deploy an IoT hub to use with your Device Simulation solution. You can always change the IoT hub your simulation uses later. Click Create to begin provisioning … Continue reading Deploy and run an IoT device simulation in Azure

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How Azure Machine Learning works: Architecture and concepts

How Azure Machine Learning works: Architecture and concepts Workflow The machine learning model workflow generally follows this sequence: Train Develop machine learning training scripts in Python or with the visual designer. Create and configure a compute target. Submit the scripts to the configured compute target to run in that environment. During training, the scripts can read from or write to datastore. And the records of execution are saved as runs in the workspace and grouped under experiments. Package – After a satisfactory run is found, register the persisted model in the model registry. Validate – Query the experiment for logged metrics from the current and past runs. If the metrics don’t indicate a desired outcome, … Continue reading How Azure Machine Learning works: Architecture and concepts

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Introduction Into Azure Machine Learning

Azure Machine Learning is a platform for operating machine learning workloads in the cloud. Built on the Microsoft Azure cloud platform, Azure Machine Learning enables you to manage: Scalable on-demand compute for machine learning workloads. Data storage and connectivity to ingest data from a wide range sources. Machine learning workflow orchestration to automate model training, deployment, and management processes. Model registration and management, so you can track multiple versions of models and the data on which they were trained. Metrics and monitoring for training experiments, datasets, and published services. Model deployment for real-time and batch inferencing. Azure Machine Learning workspaces … Continue reading Introduction Into Azure Machine Learning

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Deploy a machine learning model with the designer

Create a real-time inference pipeline To deploy your pipeline, you must first convert the training pipeline into a real-time inference pipeline. This process removes training modules and adds web service inputs and outputs to handle requests. Create a real-time inference pipeline Above the pipeline canvas, select Create inference pipeline > Real-time inference pipeline.Your pipeline should now look like this:When you select Create inference pipeline, several things happen: The trained model is stored as a Dataset module in the module palette. You can find it under My Datasets. Training modules like Train Model and Split Data are removed. The saved trained model is added back into the pipeline. Web Service Input and Web Service … Continue reading Deploy a machine learning model with the designer

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Predict automobile price with the designer: A regression problem

Create a new pipeline Azure Machine Learning pipelines organize multiple machine learning and data processing steps into a single resource. Pipelines let you organize, manage, and reuse complex machine learning workflows across projects and users. To create an Azure Machine Learning pipeline, you need an Azure Machine Learning workspace. In this section, you learn how to create both these resources. Create a new workspace In order to use the designer, you first need an Azure Machine Learning workspace. The workspace is the top-level resource for Azure Machine Learning, it provides a centralized place to work with all the artifacts you … Continue reading Predict automobile price with the designer: A regression problem

What is Azure Machine Learning?

Azure Machine Learning can be used for any kind of machine learning, from classical ml to deep learning, supervised, and unsupervised learning. Whether you prefer to write Python or R code or zero-code/low-code options. Start training on your local machine and then scale out to the cloud. The service also interoperates with popular open-source tools, such as PyTorch, TensorFlow, and scikit-learn. Machine learning tools to fit each task Azure Machine Learning provides all the tools developers and data scientists need for their machine learning workflows, including: The Azure Machine Learning designer (preview): drag-n-drop modules to build your experiments and then deploy pipelines. … Continue reading What is Azure Machine Learning?