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

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

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