Meaning of Azure Machine Learning
Azure Machine Learning, a cloud-based service, is at the core of artificial intelligence and many emerging applications - designed for creating and managing machine learning solutions. Data scientists and machine learning engineers use Azure ML to harness their existing data processing and model development skills & frameworks. Additionally, it helps them scale, distribute, and deploy their workloads seamlessly to the cloud. The tech-driven AML offers a simple point-specify-click interface to create an ideal environment for data scientists and create code-free machine learning models.
Whether you want to construct a model, conduct experiments to model deployment, or deploy a model, Microsoft’s technology provides end-to-end machine learning capabilities in the cloud. Additionally, the platform enables coding in Python and R via Jupyter notebooks, Jupyter Lab, and R Studio for different user preferences.
Key capabilities of Azure Machine Learning
Some of the azure machine learning capabilities are mentioned below:
Compute: Aure Machine Learning enables numerous compute options for various machine learning applications. Users enjoy on-demand computing that they can customize based on their use with Jupyter notebooks, R Studio, and Jupyter Labs.
Users can create a compute cluster for workloads that require a lot of processing power. Databricks, HDInsight, and Azure ML clusters are among the cluster options supported. For demanding machine learning workloads like Natural Language Processing, the compute clusters provide GPU-enabled computation choices (NLP).
Datastores: Azure Machine Learning provides datastores that may be used to mount data from Azure Storage services like a data lake store. Users can use the workspace and datastore class to access datastores from the UI or from Python code. Once data is mounted, users can read data from data lake stores into Azure ML notebooks via the datastore.
Notebooks: Jupyter notebooks, Jupyter Labs, and R Studio are all supported by Azure ML's notebooks functionality. Depending on the machine learning use case, users can choose to open an existing Jupyter notebook kernel or construct a custom kernel. Conda virtual environments are supported by notebooks, allowing for the creation of team-specific development environments. GitHub is also integrated with Notebooks. To design, train, test, and deploy models, users with access to an Azure ML instance can work together within a notebook(s).
Designer GUI: The Azure ML Designer tool provides an interactive GUI for specifying and creating machine learning models. During model development, the Designer provides a number of pre-built modules from which users can choose. A user can attach a dataset to numerous prebuilt modules, including "select columns," "clean missing data," "divide data," "two-class decision forest," "train model," "score model," and "evaluate the model." The user can then use a compute cluster to deploy the modules as a pipeline. The model results can also be viewed on a dashboard, which is created when the "evaluate model" module gets executed.
Automated ML: Users can utilize the Automated ML functionality to execute automated model trials in order to fine-tune and train an existing model to meet a user-defined target metric. Within a classification model, for example, a user can indicate that numerous automated tests be done to improve the model's "accuracy" metric. Each experiment will build on the previous one to ensure that the model is as accurate as possible. Users will be able to evaluate all of the experiment results.