Microsoft Azure Services

Microsoft Azure is Microsoft's cloud platform. It offers over 200 cloud-based products and services for building, deploying, and managing applications. This is done through Microsoft's global network of data centers.

Organizations use Azure to host websites, run virtual machines, store data, build AI applications, analyze big data, and handle disaster recovery. They can do this without the need to maintain their own physical infrastructure.

This article explores Microsoft Azure services in-depth. Let’s jump in.

Table of Contents:

• Azure Machine Learning
• Microsoft Machine Learning for Apache Spark (SynapseML)
• Azure Application Insights
• Azure Automation
• Other Key Microsoft Azure Services
• Frequently Asked Questions

Azure Machine Learning

Azure Machine Learning is a service that you can use to train, deploy, manage, and automate Machine Learning (ML) models.

• Azure Public Cloud - An Overview:

Microsoft Azure is Microsoft’s public cloud computing platform that offers a variety of cloud services. These services include platforms for computing, networking, storage, analytics, IoT, and Artificial Intelligence (AI) application development.

You can easily choose a service, create a scalable application, test, and deploy it. And the best part is, you will only pay for what you use.

• Machine Learning - An Overview

Machine Learning refers to techniques for training models on existing data to extract valuable insights. By leveraging ML models and provided data, you can predict future behaviors, trends, and outcomes. ML algorithms can learn without being explicitly programmed.

Examples:

• • You can get personalized recommendations when shopping online using ML-based predictions.
  • Credit or debit card transactions, where ML models compare information against a transaction database to identify fraudulent transactions.

Well, you must be wondering how these models can provide accurate outcomes. Let’s understand this using Azure Machine Learning, which will help you gain a crystal-clear view of ML model development and deployment.

• Azure Machine Learning (AzureML)

AzureML allows you to create, test, manage, deploy, migrate, or monitor ML models in a scalable cloud-based environment. It supports thousands of open-source packages available in Python, such as TensorFlow and Matplotlib.

The supported ML tools make it easy to explore, transform, create, and test data models. Examples are the Azure Machine Learning extension for Visual Studio Code and Jupyter notebooks. Azure Machine Learning helps automate model generation and tuning to develop efficient, accurate models.

The best part of the Azure Machine Learning service is that you can train your model on your local machine and then deploy it to the cloud. Azure offers computing services such as Azure Databricks, Azure Machine Learning Compute, and advanced hyperparameter tuning, enabling you to build efficient models.

Once you have created the right model, it’s time to deploy it in containers like Docker, which makes it easy to deploy it to Azure Kubernetes Service. You can manage deployed models and monitor executions to achieve the best outcomes. 

• Latest Capabilities of Azure Machine Learning

• • AzureML Prompt Flow: It is a visual and code-first orchestration tool that helps you build, test, evaluate, and deploy Generative AI (GenAI) applications using Large Language Models (LLMs).
    
    You can create workflows by connecting prompts, Python code, LLMs, and external data sources into a reusable pipeline.
    
    
  • Azure AutoML Enhancements: Azure AutoML automates many steps in the Machine Learning lifecycle, including data preprocessing, feature engineering, algorithm selection, hyperparameter optimization, model evaluation, and more.
    
    This tool allows data scientists to create high-quality models with little manual effort.
    
    
  • Serverless API Endpoints - These endpoints offer Model-as-a-Service (MaaS) inference, letting you access pre-trained foundation models via managed REST APIs. You don’t need to provision or manage any infrastructure.
    
    
  • Azure AI Foundry: It is Microsoft's platform for building, customizing, evaluating, and deploying Generative AI (GenAI) applications and AI agents.
    
    It combines foundation models, AI tools, prompt engineering, evaluation, and deployment capabilities into a single environment, helping you accelerate AI application development.

Additionally, Azure Machine Learning offers the following key capabilities to users:

• • The Azure Machine Learning service can automatically train and tune a model.
  • The Azure Machine Learning software development kit (SDK) for Python allows us to create and train accurate deep learning and ML models in an Azure Machine Learning workspace.
  • Various ML components can be accessed through Python packages such as scikit-learn, PyTorch, MXNet,  and TensorFlow.
  • Azure AI ML SDK v2 can be used to manage workspaces, jobs, models, endpoints, data assets, and environments.
  • It will help you evaluate model metrics, redeploy modified versions, and track models.
    
    

• Azure Machine Learning Service: Architecture:
  
  • Workspace:

The workspace is the top-level, centralized resource for the Azure Machine Learning service. It allows you to create all the artefacts for your work. It stores the training execution log, metrics, snapshots, and outputs. This data will help you choose the best training model for your work.

The model is registered through the workspace. This registered model and scoring script are used for creating a container image. This image is deployed as a REST-based HTTP endpoint.

You can use Azure Kubernetes Service to deploy this image, and also deploy it to an Azure IoT Edge device, where it runs as a module. Know that  Azure IoT Edge 1.6 is the current LTS release as of July 2026.

You can use multiple workspaces and share each with your team members. You can assign multiple roles to users, such as Owner, Reader, or Contributor.

A newly created workspace automatically creates all Azure resources such as:

• • • Azure Storage Account: The default data store.
    • Azure Key Vault: It keeps keys used by compute targets and various other useful information. It now supports a Managed HSM (Hardware Security Module) for FIPS 140-2 Level 3 compliance, while keeping secrets versioning and certificate management unchanged.
    • Azure Application Insights: It keeps monitoring data from your model.
      
      
  • Model:

A model is the code script that takes input and produces output. Developing an ML model requires selecting algorithms, feeding data to it, and tuning hyperparameters. Training is an iterative process that produces a trained model that retains what it has learned.

A model is obtained by executing it in Azure Machine Learning. If you have created your model by training it outside, you can still use it in Azure Machine Learning. All it requires is registering it in the Azure Machine Learning workspace.

• • Model Registry:

A model registry is responsible for keeping records. It records all the models in your Azure Machine Learning workspace. You can recognize a model by its version and name.

Every time you register a model with a name that has already been used before, the registry stores it as a new version. The version number is incremented, and the model name remains the same.

You can also add additional metadata tags when registering your model. It helps you search for it through that tag. Always remember that if a model is used in an image, it can’t be deleted.

• • IoT module

An IoT module is a Docker container that hosts your model, program/application, and various other dependencies. These modules are deployed on edge devices using Azure IoT Edge.

If you’ve selected a monitoring feature, Azure will collect telemetry data from the models. This model lies inside the Azure IoT Edge module. This data can be easily accessed inside the storage account instance. The execution of the module is taken care of by Azure IoT Edge. The main device that hosts your model is also monitored by it.

• • Datastore

A datastore provides a storage abstraction for your Azure account. It leverages Azure File Share and an Azure Blob Storage container to store data. Each workspace keeps a default datastore.

Here, you can register other data stores on demand. To access this information, you need the Python SDK API. You can also use Azure CLI v2 to retrieve files from here.

• • Run Record

A run record stores information such as execution metadata, metrics logged by your program, and output files, which are automatically collected by the experiment. A run is produced when you provide a program file for training your model.

A run contains zero or more child runs. For example, a top-level run can have two child runs, each with their own child runs.

• • Experiment

The experiment includes multiple runs of a particular program. It is available in a workspace. Every time you provide a run, you have to give an experiment name. All the run data is stored in an experiment.

Suppose you submit a run and you provide an experiment with a name that doesn’t exist; it automatically creates a new experiment with the provided name.

• • Pipeline

A Machine Learning pipeline is responsible for creating and directing the workflow involved in Machine Learning phases. For example, a pipeline handles data preparation, model deployment, training, and inference. Each phase in the pipeline contains various steps; all of them run independently on multiple compute targets.

• • Compute Target

A compute target is a computing resource used to execute your training program or host your service during deployment. They are associated with a workspace. Compute targets, apart from the local machine, are shared among workspace users. It also includes compute instances, compute clusters, serverless compute, and managed Online/Batch endpoints.

• • Managed and unmanaged compute targets

Managed compute targets are those created and managed by the Azure Machine Learning service. They are optimized to handle workloads. Through the Azure portal, Azure Machine Learning SDK or Azure CLI, you can easily create a Machine Learning compute instance in a workspace. Other compute targets must be created outside the workspace; once developed, they are attached to it.

The compute targets that are not managed by the Azure Machine Learning service are called unmanaged compute targets. They are created outside the workspace and attached to it for further use in the Azure Machine Learning service. It requires extra steps to maintain or enhance their performance when handling ML workloads.

• • Run configuration

It keeps the instruction set to specify how your program executes on a compute target. It contains a long set of behavior definitions, such as which environment to use when building environments using Python or Conda specifications. It is persistent in a directory that includes the training program.

• • Training script

It requires a directory containing training scripts and other related files during model training. You need to provide an experiment name to store the data collected during training.

The entire directory is cloned into the training environment during training. The script named by the run configuration is executed at the beginning. Finally, a directory snapshot is stored in the experiment workspace.

• • Logging

The Azure Machine Learning Python SDK is used to collect log metrics. After execution, you can make queries to identify which run was obtained during deployment.

• • Snapshot

When the run is provided, the Azure Machine Learning SDK compresses the directory containing the script. This zip file is then sent to the compute target where it is extracted and executed.

Azure Machine Learning keeps this file as a snapshot to provide you with the run record. You can easily download this snapshot from a run record. Mainly, ML SDK v2 focuses on jobs, environments, and command jobs.

• • Activity

Activity points to long-running operations, such as creating or deleting compute targets or executing a script on a compute target. It provides notifications via the SDK, making it easy to monitor operational progress.

• Managing, Deploying, and Monitoring Models

Next, we will discuss how the Azure Machine Learning service is used to deploy, manage, and monitor your Machine Learning model. You can deploy your developed and trained ML model on a local machine or any other source.

The diagram below represents the whole deployment workflow:

The deployment process involves the following phases:

• • Registering the model in a registry hosted in an Azure Machine Learning workspace.
  • Package the model into a portable container image. The image pairs a model with the scoring script and other associated dependencies.
  • Deploy the image to a cloud service. Or, it can be deployed on edge devices as well, where the image should be deployed.
  • After deployment, monitor and gather data.
  • Finally, update the deployment to use a new image.

All the steps involved are performed independently. You can also integrate the deployment with a CI/CD workflow, as shown in the picture below.

• Register your model:

During model registration, the version is assigned to your model and stored in the Azure cloud workspace. By registering a model, you can easily manage and track your trained models. Its name and version can be used to recognize the registered model.

The model version is auto-incremented while registering a model with the same name. As we mentioned earlier, model registration includes additional metadata tags that help find your model.

The model that is used by an image can’t be deleted at the same time.

• Monitor models and collect data:

To monitor input, output, and other sensitive information, you can use an SDK for model logging and data capture. You can also use Azure AI Foundry evaluation, Prompt Flow tracing, Azure Monitor, or OpenTelemetry integration.

You have to import the SDK into the scoring script or into your application that uses the SDK. After importing it, you can use it to log information such as parameters, outputs, or input details.

Once you have enabled model data collection during image deployment, the information required for data collection, such as your personal blob store authentication keys, logs, etc., is automatically provisioned.

Use the following code for testing.

• Update the deployment

The updates you introduced to your model are not automatically registered. Just like updates, new image registrations don’t automatically update deployments created under the previous image version. You have to manually register the image and model, then update the model. For updating, use the following code.

• Dealing with the JSON data

The code below accepts and returns the JSON data. 

Here, the run function is responsible for transforming data from JSON to the model-expected format and transforming the response back to JSON before finally returning it.

Microsoft Machine Learning for Apache Spark (SynapseML)

As data generation scales up, it is essential to have a reliable tool for extracting valuable insights from big data. The Spark ML library is one of the best tools for processing big data at a massive scale with Machine Learning experiments.

Data scientists face challenges such as low-level data manipulation, a lack of image-processing tools, and limited text-analytics capabilities. Thanks to SynapseML for providing a powerful environment built on top of SparkML that eliminates the challenges of data science.

It includes libraries such as OpenCV to enable a smooth workflow. With it, one can create models using only one-tenth of the code, using pipeline objects. It is a fault-tolerant, RESTful, and elastic ML framework.

• The key features of SynapseML:

• Azure AI Vision APIs: They are cloud-based REST APIs and SDKs. They allow developers to add computer vision features to applications without having to start from scratch with machine learning models.
• Face recognition: Azure AI Vision offers image analysis, OCR, object detection, tagging, captioning, and other computer vision features. You can use the Face-related features separately via Azure AI Vision Face APIs, in line with Microsoft's Responsible AI policies.
• Distributed Model Interpretability: It provides an image classifier using a distributed implementation. It leverages Local Interpretable Model-Agnostic Explanations (LIME).
• Millisecond serving: It serves Spark computations in milliseconds via Python, Scala, or any other language.
• Scalable Deep Learning: A distributed computation framework through Apache Spark allows you to deal with deep learning problems through a scalable environment.
• Distributed Microservices: There are tools available to establish HTTP endpoints with Spark, which can serve as an elastic microservice orchestrator.

Azure Application Insights - An Overview:

Application Insights is primarily used to monitor live web applications and automatically detect performance anomalies. Advanced data analytics tools help in diagnosing application issues.

You can understand what users are doing in your app and what needs improvement to make it perform better and increase usability. It supports a wide array of platforms, including on-premises and cloud, and integrates with your internal DevOps processes.

• Azure Application Insights with a Log Analytics Workspace

Log Analytics Workspace in Azure Monitor enables a modern monitoring setup. It stores Application Insights telemetry in a Log Analytics Workspace instead of the old standalone Application Insights resource.

Organizations can centralise application, infrastructure, and security logs in one place using a workspace-based Application Insights resource. It enables improved analytics, monitoring, alerting, and troubleshooting with Kusto Query Language (KQL).

• Azure Monitor - A Deep Dive

Azure Monitor offers monitoring solutions known as Insights, including prebuilt dashboards, performance metrics, dependency maps, and more.

Let’s learn more about Azure Monitor.

• • Container Insights: Azure Monitor Container Insights helps you monitor the health and performance of containerized applications running in Azure Kubernetes Service (AKS), Azure Arc-enabled Kubernetes, and other supported Kubernetes clusters. 
  • VM Insights: Azure Monitor VM Insights provides monitoring for Azure Virtual Machines, Virtual Machine Scale Sets, and hybrid servers. 
  • Network Insights: Azure Monitor Network Insights provides you with a clear view of Azure networking resources and connectivity.
• Azure Monitor OpenTelemetry Distro - An Overview

Azure Monitor OpenTelemetry Distro is a distribution of Microsoft’s OpenTelemetry observability framework. It makes it easier to instrument applications for monitoring with Azure Monitor.

It offers preconfigured SDKs and exporters that automatically gather traces, metrics, and logs, which are sent to Application Insights and Log Analytics.

• Azure Application Insights Implementation Process

Install and set up an Application Insights resource in the Azure portal. The Insights resources monitor your app and send the data to the portal.

In the Azure portal, you can apply the integrated data for analysis.

• The Application Insights Monitor - An Introduction:

Application Insights helps you understand how your app is performing and how it's being used.

This tool examines the following processes within the software application.

• • Identify which pages are most popular, at what times of day, and where your users are. Besides, identify which pages perform the best in the request-response cycle of your software.
  • External services and servers will be monitored if they are slowing down your work.
  • Analyse the statistics, identify specific instances, and retrieve the stack trace and the requests received for that particular process.
  • Page views on your apps and app loading performance
  • Monitor conversion rates, response times, click rates, downloads, and failure rates for AJAX calls for your web page.
  • Provides information about the user login activity and session time while on the website.
  • Diagnostic event logs from your app
  • Custom events and metrics that you write yourself to track specific business events
    
    
• Limitations of Azure Application Insights
  
  • Limitations on monitoring specific URL or action requests, SQL query error percentages, etc.
  • Email Alerts and SMS escalation do have certain implementation limitations.

Azure Automation:

Automation is the use of machines to perform tasks that humans previously performed. Automating a repetitive task or process reduces human intervention. It helps increase output, lower operating costs, and improve ROI.

Azure Automation delivers cloud-based automation, operating system updates, and configuration management that enables consistent management. It provides an extensive automation service that enables you to automate tasks that would otherwise take valuable time away from IT personnel.

Let’s dig deeper into Azure Automation:

• • Azure Automation is a Software-as-a-Service (SaaS) offering that provides a scalable, reliable automation platform for all IT-related operations across an organization.
  • It can be configured to interact with and automate on-premises infrastructure. The component that enables this feature is called an Azure Hybrid Runbook Worker.
  • It allows multiple users to access and manage configuration changes from Windows and Linux systems.
  • Organizations can standardise common processes with automation in Azure and reduce error-prone manual tasks.
    
    
• Azure Runbook - An Introduction:

An Azure Runbook is typically a compilation of routine IT procedures and operations required by administrators. They run on the Azure cloud platform but do not have access to on-premises resources. The Hybrid Runbook Worker feature of Azure Automation allows you to run Runbooks directly on the computer hosting the role.

The Hybrid Runbook Workers interact with local resources in the environment, and their configuration and management are stored and managed in Azure Automation. They are delivered to one or more designated Hybrid Worker computers over VPN in a cloud environment or over the internet on on-premises devices.

• Azure Automation – Key Highlights:

Microsoft Azure Automation is a simple, fast cloud automation platform. It comes packed with significant features such as:

• Azure Automation State Configuration

Azure Automation State Configuration is a feature that allows you to define, deploy, monitor, and maintain the desired configuration of servers and virtual machines. It is built on PowerShell Desired State Configuration (DSC), which helps ensure that systems stay consistent and compliant.

With State Configuration, administrators can automate configuration management and apply standardized settings across Azure and hybrid environments.

• Azure Machine Configuration

Azure Machine Configuration is an Azure Policy feature that checks, enforces, and tracks operating system (OS) settings on Azure virtual machines and Azure Arc-enabled servers. 

It helps organizations stay compliant by ensuring that machines adhere to established security, configuration, and governance rules. It is also used in many Desired State Configuration (DSC)-based governance scenarios.

It allows organizations to leverage their PowerShell DSC expertise while embracing Azure Policy-based compliance management.

• Hybrid Worker Extension Model in Azure Automation

This model is the current approach for deploying Azure Automation Hybrid Runbook Workers. Rather than manually installing and registering Hybrid Runbook Workers, Azure uses the Azure VM Extension for Azure VMs or the Azure Connected Machine agent for non-Azure servers to deploy and manage them.

This model makes installation easier, boosts security, and offers a more consistent management experience for both Azure and hybrid environments.

• How Does Azure Automation Work for an Enterprise?

Azure Automation tools virtualize enterprise infrastructure. They allow enterprises to streamline their IT workloads, reduce manual effort, and automate backup and recovery processes.

The most important function is adjusting scalability according to usage. Adjusting the environment during holidays to the reduced workload is one of the simplest Azure Automation examples.

• Benefits of Azure Automation

Azure Automation Service simplifies cloud management and automation by optimizing your existing investment and integration skills. Some of the benefits of Azure Automation are:

• • Lowers costs: Your routine and time-consuming cloud management tasks are often error-prone. Azure Automation helps reduce errors and increase efficiency, reducing operational costs and saving time.
  • Optimizes workflows: Azure Automation lets you leverage existing workflows or customize them to create your own. You can monitor and maintain Azure resources and also create and deploy your own runbooks, as needed.
  • Integration: Azure Automation works seamlessly with websites, VMs, servers, storage, and other Azure services. It can also be used with any third-party applications, service offerings, or public Internet APIs.
  • Reliable service: Azure Automation efficiently manages systems, tools, and departments, improving your performance and saving time.
  • Reduced manual errors and dependencies: Some organizations typically maintain a huge network, along with regular hardware and software maintenance. They must hire professionals to continuously manage their systems. It may increase human error and other problems arising from dependence on manual processes. Enterprises with multiple organizational setups across different countries use Azure Automation to improve operational efficiency.
  • Secured and Holistic Information: The process, reports, and information and data are secured through automatic backup and recovery methods. So, in case of an unforeseen disaster, the data can be easily retrieved. Role-based access ensures information is under the control of authorized users. A virtual machine is automatically built to act as a maintenance window for the main system. It gives alerts for restarts and updates on an hourly, weekly, monthly, and yearly basis for maintenance.
• Automated Patching of Azure Virtual Machines:

Azure virtual machines can provide high data availability, self-healing, self-service, failure-alert systems, geo-replication, and disaster-recovery mechanisms.

• Other Automation tools similar to Azure Automation

Service Management Automation (SMA) is another automation tool similar to Azure Automation, but SMA does not have graphical runbooks. System Center 2012 Orchestrator is primarily for automating on-premises resources.

Other Key Microsoft Azure Services:

Let’s discuss other key Microsoft services further.

• Azure Service Bus: 
  • Premium Tier with Availability Zones: Azure Service Bus Premium now supports Availability Zones by default in supported Azure regions. It improves resilience by distributing messaging resources across multiple data centres. So you can maintain high availability during zone-level failures.  
  • Modern Java Spring Integration: Azure Service Bus connects to Java applications via Spring Cloud Azure, the recommended SDK for Spring developers.It makes sending and receiving messages easier, supports dependency injection and auto-configuration, and works well with Azure Identity for secure authentication using Microsoft Entra ID.
    
    
• Azure Expressroute: 

Azure ExpressRoute Direct now provides 100 Gbps of dedicated connectivity. It allows organizations to establish high-bandwidth, private connections between their on-premises systems and Microsoft Azure. 

ExpressRoute FastPath is the recommended setup for jobs that are sensitive to delays. It allows certain traffic to bypass the ExpressRoute Virtual Network Gateway. 

It reduces network delays, increases capacity, and improves application performance. It is useful for high-performance computing, large databases, real-time analytics, and other vital enterprise applications.

• Azure Stream Analytics 

Azure Stream Analytics is a fully managed real-time analytics service. It processes and analyzes streaming data from various sources. It helps you detect patterns in data, create insights, and trigger actions with minimal delay.

This service is perfect for Internet of Things (IoT), app monitoring, fraud detection, and real-time business intelligence. It now supports Apache Kafka output and integrates with Azure Synapse Analytics. It allows Edge streaming jobs to run on IoT Edge modules.

• Azure IoT Hub

It is a fully managed cloud service that enables secure, reliable, two-way communication between Internet of Things (IoT) devices and cloud applications.

Azure IoT Hub supports MQTT 5 in preview. It offers better messaging features, such as user properties, message expiry intervals, and reason codes, as well as improved error handling, than MQTT 3.1.1.

Nonetheless, Azure IoT Hub connects with Azure IoT Operations. It helps organizations manage devices, process data, and improve industrial edge capabilities across hybrid and Kubernetes-enabled environments.

• Azure Arc

Azure Arc enables Azure management and governance in on-premises, multi-cloud, and edge environments. 

• • Azure Arc-enabled Kubernetes lets you connect and manage Kubernetes clusters that run outside Azure. 
  • Azure Arc-enabled SQL Server brings Azure management services to SQL Server instances hosted on physical servers, virtual machines, or other cloud platforms. 
  • Azure Arc-enabled Data Services allows Azure data services to run on Kubernetes clusters outside Azure.
    
    
• Azure Stack Hub

Azure Stack Hub is an extension of Microsoft Azure that allows organizations to run Azure services in their own data centers or in disconnected environments. It provides a hybrid platform enabling applications and services to be developed and deployed across Azure and on-premises infrastructure using the same tools and APIs.

Azure Stack Edge is the edge compute appliance, whereas Azure Stack HCI is the hyperconverged infrastructure path.

• Azure Site Recovery

Azure Site Recovery (ASR) is a disaster recovery service that helps organizations protect their applications and workloads. It is done by replicating virtual machines and physical servers to Azure or a secondary site.

ASR now supports migration from other clouds, such as AWS EC2 to Azure, as well as Azure-to-Azure DR. It also offers improved support for Windows Server 2025 and RHEL 9 replication.

It reduces downtime and data loss by allowing automated failover and failback during both planned and unplanned outages.

Also Read: Azure Interview Questions and Answers

Frequently Asked Questions:

1) What are the main categories of Azure services?

Ans: Compute, storage, networking, databases, analytics, integration, DevOps, migration, and hybrid cloud are the main categories of Azure services.

2) Is Azure Application Insights free?

Ans: Yes, Azure Application Insights has a free tier, but it is not entirely free. It follows a consumption-based pricing model. You mainly pay for the amount of telemetry data your application collects.

3) What is Azure Arc used for?

Ans: Azure Arc is a management service for hybrid and multicloud environments. It allows you to manage and govern resources that run outside of Azure.

With Azure Arc, you can oversee on-premises servers, edge devices, Kubernetes clusters, and resources hosted by other cloud providers like AWS and Google Cloud. You can perform all these tasks from a single Azure control plane.

4) Azure Service Bus vs Azure Event Hubs — which should I use?

Ans: You can choose Azure Service Bus if you need reliable communication between applications, order processing, queue-based messaging, or payment workflows.

You can choose Azure Event Hubs if you need high-volume event ingestion, IoT telemetry collection, application log aggregation, and real-time analytics pipelines.

5) Is Azure Service Fabric being deprecated?

Ans: No. Azure Service Fabric is not being discontinued. Microsoft continues to support and update Azure Service Fabric. Recent releases, including the Service Fabric 11.x series and cumulative updates, indicate that the service is still in active support under Microsoft's Modern Lifecycle Policy.

Conclusion:

This article must have provided you with deep knowledge of Azure Machine Learning, SynapseML, Application Insights, and Azure Automation. You have clearly understood the latest Azure capabilities, hybrid management, and more in-depth.

If you want to explore more about Microsoft Azure services, you can sign up for a Microsoft Azure course with MindMajix. By the end of the training, you will become familiar with Microsoft Azure products, their benefits, and more, which will help accelerate your career in the cloud domain.