Build Machine Learning Model With Azure ML Designer

Today Microsoft provide large number of cloud-based computing services that are available to businesses, developers, government agencies and many more. One of the service as Azure Machine learning service Empower developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible ML.

The Azure Machine Learning provides data scientists and data engineers to efficiently work together on Machine Learning projects, they offering three services to develop ML models:

Automated ML – component which allows to automatically train a model based on a target metric by simply ingesting a dataset and the ML task to be executed (e.g. classification). This is my article about azure automated ML
Designer – which allows users to visually build ML workflows by dragging and dropping pre-made ML tasks into a canvas and connecting them.
Python and R SDK – which allow to build and run ML workflows from your favorite IDE or Azure ML’s built-in notebook functionality.

Lets see what is Azure Machine learning designer in this article .

What is Azure Machine Learning Designer

Azure Machine Learning designer is a visual-first environment provides a user-friendly interface for users to build and test machine learning pipelines. These pipelines can be created by dragging prebuilt machine learning modules into the interface and connecting them to form a workflow without needing to write a single line of code. This means that people can create end-to-end machine learning pipelines, train models and deploy them without writing a single line of code.

You may see a similar service announced back in June 2014 called ML Studio, now known as Azure Machine Learning Studio (classic). Designer can be thought of as ML Studio’s successor.

The existing prebuilt modules machine learning tasks that you would want to execute during the various phases of the machine learning development cycle (e.g. data preparation, feature engineering model training). However, the Designer also allows users to create their own modules in R or Python. This increased flexibility comes in handy when working on real-life machine learning projects that require data scientists to.

How we can build Machine Learning Model using Azure ML Designer Works

There are some steps to do when we building a Machine Learning models from azure ML studio.

Drag-and-drop datasets and modules onto the canvas.
Connect the modules to create a pipeline draft.
Submit a pipeline run using the compute resources in your Azure Machine Learning workspace.
Convert your training pipelines to inference pipelines.
Publish your pipelines to a REST pipeline endpoint to submit a new pipeline that runs with different parameters and datasets.
- Publish a training pipeline to reuse a single pipeline to train multiple models while changing parameters and datasets.
- Publish a batch inference pipeline to make predictions on new data by using a previously trained model.
Deploy a real-time inference pipeline to a real-time endpoint to make predictions on new data in real-time.