Azure Machine Learning is a Machine Learning service that helps to build and deploy models faster. These models are built and trained in Azure Machine Learning Studio.
In this post, we will learn more about Azure Machine Learning Studio.
What Is Azure Machine Learning (ML) Studio?
Azure ML Studio is a workspace where you create, build, train the machine learning models. It is a drag and drop tool (Azure Machine Learning Designer) where you can drag the data sets and further process the analysis on that data. It offers both no-code and low-code options for projects.
ML Studio (classic) was the first drag and drop tool which was a standalone service that offered visual experience but however, it does not interoperate with Azure Machine learning. It was released in 2015. ML Studio (classic) does not support Code SDKs, ML pipeline, Automated model training and has a basic model for MLOPs and many other features were missing that is a part of Azure Machine Learning Studio now.
Authoring Platforms Offered By Azure ML Studio
Azure Machine Learning Designer(preview): It is a drag and drop tool where we can drop datasets and modules for creating ML pipelines.
Notebook: Azure ML Studio has Jupyter Notebook Servers which are directly integrated into the studio where you can write your own code.
Automated Machine Learning UI(preview): It is an easy to use interface used for training and tuning the model.
Features Of Azure ML Studio
- It is fully integrated with Python and R SDKs.
- It has an updated drag and drop interface generally known as Azure Machine Learning Designer(preview).
- It supports MLPipelines where we can build flexible and modular pipelines to automate workflows.
- It supports multiple model formats depending upon the job type.
- It has Automated model training and hyperparameter tuning with code first and no-code options.
- It supports data labeling projects.
Architectural View Of Azure ML Studio
The Azure ML studio provides a view of all the artifacts in the workspace. We can get a detailed view of the details and results of our experiments, pipelines, datasets, model, etc.
It has the following Components:
1. Notebooks– It is used to write and run code in integrated Jupyter notebooks. It is useful for any workspace and supports multiple languages including Python, R, F#, etc.
- It requires no installation and is a free service.
2. Automated ML– It is used for training and tuning the model. In other words, it is a process of automating the iterative, time-consuming tasks of machine learning model development.
- It helps the developers in building ML models with high efficiency, scale, and productivity all the while sustaining model quality and also saves time and resources.
3. Designer– It is an interactive interface to connect datasets and modules to create machine learning models.
- It is a drag and drop tool where you can drop the datasets and modules and connect the modules to create a pipeline (Pipeline consists of datasets and modules which are connected).
4. Datasets– Used for easy access to data for the model. They create a reference to the data source location and create a copy of its metadata.
- Datasets can be created from datastores, Azure Open Datasets, or public URLs.
- They are of two types: FileDatsets and TabularDatasets and both can be used in Azure Machine Learning Training workflows.
5. Experiments– It is used for grouping runs for a specified script. It represents the main entry point for creating and working with the experiments in azure.
- It always belongs to a workspace and if an experiment is not found in the namespace then a new Experiment is created.
- Information for a run is stored in the experiment for which a name is provided before submitting the run.
- The name must be 3-36 characters and must start with a letter or a number.
6. Models– It is a piece of code that takes input and produces the output for the given inputs.
- Creating a model in machine learning involves selecting an algorithm, providing it with data, and tuning the hyperparameters (Hyperparameters are adjustable parameters that are selected to train a model that govern the training process itself).
- While creating a model any popular machine learning framework can be used such as PyTorch, TensorFlow, Scikit-learn, etc. A model can be trained outside the Azure Machine Learning or in the Azure Machine Learning.
7. Endpoints– It provides remote access to the services that are running.
- It is an instantiation of the created model into either an IoT module for integrated device deployments or web services that can be hosted in the cloud.
8. Compute- A Compute/Compute Target is a machine or a set of machines that are used to run the training scripts or host service deployments. A local machine or a remote compute resource can be used as a compute target.
- With the help of computing targets, the training can be started on local machines and then scaled out to the cloud without changing the training script.
- There are 2 fully managed cloud-based Virtual Machines that are configured for machine learning tasks: Compute Instance and Compute Clusters.
9. Datastores- Datasets use datastores for secure connection to the Azure storage services. They store connection information without putting the integrity of the original data source and authentication credentials at risk.
10. Data Labeling- It provides a central place to create, manage, and monitor labeling projects and tracks progress and maintains a queue of incomplete labeling tasks.
- Machine Learning supports image classification and object identification.
- The labeled data can be viewed and exported in COCO format or as an Azure Machine Learning dataset.
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