Microsoft Azure gives us the freedom to build, manage & deploy applications on a massive global network. At its core, it provides Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) to provide solutions at scale.
Azure ML Studio is a cloud service(Workspace) for boosting & managing the Machine Learning lifecycle, & helps with Data-workflows for professionals. It simplifies the development and deployment of machine learning models and services.
It offers both no-code and low-code options for projects.
Here’s an overview of few unique features that the Azure ML Studio provides:
Azure Machine Learning Designer: It is a drag and drop tool to drop datasets and modules to create ML pipelines without writing a single line of code.
Notebook: Microsoft Azure ML Studio has Jupyter Notebook Servers which are directly integrated into the studio where you can write your code, & run scripts.
Azure AutoML: This service is capable of automating the time-consuming, iterative tasks of machine learning model development for classification, regression, and forecasting tasks.
Azure SDK: It is fully integrated with Python and R SDKs, for running scripts.
Endpoints: Azure ML endpoints provide a simple interface for creating and managing model deployments.
Compute Resources: Compute Instances are managed cloud-based workstations for data professionals, where you run your training script or when you host your service deployment.
Datasets/Datastores: It Provides a secure connection to the Azure storage services & Data Sources.
Azure ML Studio provides MLOps capabilities for the users in ML/Data Science projects, from building scalable ML models to uploading & analyzing datasets, running scripts & experiments & viewing Run Logs and metrics & deployment at Scale.
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