Azure is a web-based management interface provided by Microsoft Azure, a cloud computing platform and service provided by Microsoft. It serves as a central hub for managing and monitoring Azure resources, services and solutions.
Azure Portal allows users to interact, manage, and access various Azure services.
Things can be done on Azure portal:
Resource Management: Manage and remove Azure resources like virtual machines, data webs, and more through a user-friendly interface.
Dashboard: Users can customize their dashboard to display the most important information and metrics for their Azure resources. Helps monitor and manage work resources effectively.
Services and Marketplace: Access to a wide range of Azure services and solutions, including third-party offerings from the Azure Marketplace.
Regulation and enforcement: Manage security access, implement security, and provide tools to ensure regulatory requirements.
Cost management: Users can track and manage their Azure costs, set budgets, and monitor resource costs.
Monitoring and Diagnostics: Access Azure Monitor and App-In-Sites to focus on app and monitoring visibility and health.
Role-advised access control (RBSC): Azure Financial supports RBS, which allows you to control who can access and manage Azure resources.
Resource groups: To help organize resource groups, it can be easier to manage linked resources as a unit.
Notifications: Users can provide alerts and notifications for specific events or scenarios in their Azure resources.
DevOps Mention: Azure can go mainstream with DevOps and other development tools to support continuous integration and deployment (CI/CD) new pipelines.
Automation: Azure can be integrated with Azure and Azure Apps to create workflows and automate functions.
Azure is well-designed to be delivered through a web browser, which is optimized for delivery across its various devices and platforms. It provides a unified and simple way for novice and graphic users to expertly interact with and manage their Azure Cloud resources.
If you’re new to Azure, Azure is a great place to start. It provides a simple and easy-to-use interface for managing your Azure resources. As you become more familiar with Azure, you can leverage Azure’s advanced capabilities . I would like to recommend that you pursue Azure certifications such as the solution architect az-104 and the cloud practitioner az-900.
Some benefits of using Azure:
Centralized management: Azure provides a centralized view of all your Azure resources, making it easier for you to manage them.
Ease of use: Azure is designed to be easy to use even for beginners.
Secure: Azure provides a secure environment for managing your Azure resources.
Scalable: Azure can be scalable to meet your business needs.
Cost Effective: Azure is an efficient way to manage your Azure resources.
If you deploy your Azure in a powerful and easy-to-use management interface, Azure resources can be leveraged in one go.
Azure Machine Learning
“Azure Machine Learning” service is a cloud-based platform provided by Microsoft Azure that provides a wide range of tools and services for building, training, deploying and managing machine learning models. You can use Azure Machine Learning for a variety of machine learning-related tasks, including data preparation, model training and evaluation, and deploying models as web services or containers.
Here are some of the key features and steps you can perform in the Azure Machine Learning service through the Azure portal:
Create a workspace:
To get started with Azure Machine Learning, you typically start by creating a Machine Learning workspace in the Azure portal. This workspace serves as a central location for managing your machine learning resources.
Data Preparation:
You can use Azure Machine Learning to explore and preprocess your data. Azure offers data storage options such as Azure Data Lake Storage or Blob Storage to store your datasets.
Usage:
You can create machine learning experiments in Azure Machine Learning using various programming languages like Python. These experiments may include tasks such as model training, hyperparameter tuning, and tracking metrics.
Model training and evaluation:
Azure Machine Learning supports various machine learning frameworks like TensorFlow, PyTorch, Scikit-Learn etc. You can use these frameworks to train your machine learning models on cloud-based compute resources. Azure also provides tools to monitor model performance and compare different models.
Model Deployment:
Once you have a trained model, you can deploy it to Azure as a web service or container. It allows you to make predictions using your model via a REST API.
Monitoring and Management:
Azure Machine Learning provides tools for monitoring deployed models, including tracking performance and managing versions of models.
AutoML: Azure AutoML is a feature that automates most of the model selection and hyperparameter tuning process, making it easy to build machine learning models with minimal coding.
Integration with other Azure services: Azure Machine Learning can be integrated with other Azure services such as Azure Databricks for big data processing, Azure DevOps for CI/CD pipelines and Azure Synapse Analytics for data warehousing.
Security and Compliance:
Azure provides security features to protect your data and models, as well as compliance certificates to meet various regulatory requirements.
you can find more details on Microsoft website azure portal for Microsoft relate contents check out cloud fusion blogs