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MSDEVBUILD - Community of Microsoft AI, Azure and Xamarin by Suthahar - Solution Architect for Microsoft AI, Azure, Xamarin | Tech Author and Speaker
If you have used Microsoft AI tools in the past, you might remember popular services like Cognitive Services and the Bot Framework. These tools were widely used to add features like speech recognition, vision analysis, and chatbots to applications.
But things have changed a lot. As of 2025, Microsoft now offers many new AI services with different names including Azure OpenAI, Microsoft Copilot, Azure AI Studio, Azure OpenAI Studio, Azure AI Services, and Azure AI Foundry.
Formerly known as Cognitive Services, this was Microsoft original collection of pre-trained, ready-to-use AI APIs. In 2023, it was renamed to Azure AI Services to align with Microsoft rebranding strategy and to unify all prebuilt AI capabilities under a single banner.
These services are great for quickly adding AI features for Vision, Speech, Language and Decision without training a model yourself. Instead of writing complex ML code, developers can just call an API.
Introduced in 2021 as part of the partnership between Microsoft and OpenAI, Azure OpenAI Service gives you access to powerful language models like ChatGPT (GPT-4, GPT-4o) with enterprise level security. It extends foundational AI capabilities originally offered through OpenAI APIs and brings them into the enterprise Azure ecosystem.
Azure OpenAI supports only OpenAI models (GPT family), while broader support for other foundation models like Meta’s LLaMA, Mistral, and Google’s Gemma (Gemini) is handled by Azure AI Foundry.Azure OpenAI is integrated with other Microsoft services like Azure AI Studio (for workflow orchestration)
Microsoft Copilot (for embedding in Office tools), making it a central part of Microsoft generative AI platform. Its use cases span across business functions like automating customer support, content generation, code assistance, and enterprise search.
You want to build a chatbot, summarizer, or custom Copilot using GPT. Common business use cases include customer service chatbots, automated support agents, sales assistance tools, HR knowledge bots, marketing content generators, and internal productivity copilots that summarize documents or generate emails. These solutions help reduce human workload, speed up communication, and increase customer satisfaction across industries like retail, banking, healthcare, and logistics.
Introduced in 2024 as the evolution of Azure OpenAI Studio, Azure AI Studio is a no-code/low-code interface designed to simplify building AI-powered solutions. The previous name, Azure OpenAI Studio, is now retired and replaced by Azure AI Studio to reflect a broader vision beyond OpenAI models. It still retains OpenAI integration but now also supports orchestration across Azure AI Services and external enterprise connectors.
Azure AI Studio does not directly integrate with Azure AI Foundry but complements it. While Foundry handles model lifecycle, governance, and large-scale deployments (especially for multi-model environments like Meta, Mistral, or Gemini), AI Studio is focused on app building and orchestration. Both services often work together: models deployed and managed via Foundry can be used in workflows created in AI Studio.
This separation of responsibilities allows businesses to prototype in AI Studio and later scale and govern their models using Azure AI Foundry.
Azure AI Studio extends and integrates features from Azure OpenAI, Azure AI Services, and enterprise connectors. It allows users to build AI workflows visually, combine OpenAI models with enterprise data, and create custom copilots or agents that respond to real-time inputs.
It supports prompt engineering, orchestration logic, evaluation, and deployment all in one place. Over time, Azure AI Studio has absorbed earlier features from tools like QnA Maker (via Azure AI Language) and Azure Cognitive Search integration.
Azure Machine Learning, also known as Azure ML, has been around since 2018 and remains a core component of Microsoft's AI platform. Unlike other Azure AI services focused on prebuilt or foundation models, Azure ML is designed for building, training, and deploying custom machine learning models, especially using structured/tabular data.
It has not been renamed but has evolved to integrate with newer services like Azure AI Foundry. Azure ML pipelines and assets (datasets, models, environments) can now be managed, deployed, or governed via Foundry, allowing better model lifecycle handling across hybrid teams and AI workloads.Azure ML supports AutoML (automated model building), responsible AI (fairness, transparency), model explainability, MLOps (CI/CD for ML), and deployment to edge devices.
Many businesses across industries are using Azure AI to solve real-world challenges and improve productivity. In retail, companies use AI to personalize shopping experiences and automate customer service.
It does not replace any previous single tool but instead extends the AI ecosystem by enabling businesses to manage multiple foundational models not just OpenAI, but also Meta LLaMA, Google Gemma, Mistral, Hugging Face, and custom models from a unified control plane.
Foundry is not a renaming of an old tool—it’s a new platform aimed at model lifecycle management and cross-model orchestration.
This blog provides an in-depth explanation of object and dynamic types in C#.
object is the base class for all data types in C#.
using System;
class Program
{
static void Main()
{
object obj = 10; // Boxing
int num = (int)obj; // Unboxing
Console.WriteLine(num);
}
}
object stores any data type.dynamic allows runtime type flexibility.
using System;
class Program
{
static void Main()
{
dynamic value = 10;
Console.WriteLine(value);
value = "Hello World";
Console.WriteLine(value);
}
}
dynamic can change its type at runtime.object and dynamic| Feature | object | dynamic |
|---|---|---|
| Type Checking | Compile-time | Runtime |
| Casting Required | Yes | No |
| Performance | Slower due to boxing/unboxing | Faster as it avoids compile-time checks |
| IntelliSense Support | Yes | No |
object and dynamic in C#?A: object requires explicit casting, and type checking is done at compile time, while dynamic skips compile-time checking and determines types at runtime.
dynamic?A:
dynamic over object?A: Use dynamic when working with reflection, COM objects, or dynamic languages like Python.
dynamic affect performance?A: Yes, dynamic operations take longer due to runtime type resolution, whereas object may perform better when using proper casting.
dynamic store primitive types?A: Yes, dynamic can store any type, including primitive types, and allows changing them at runtime.
object when dealing with generic types and need type safety.dynamic when working with unknown types at runtime.dynamic.This guide provides an essential understanding of object and dynamic types, helping developers choose the right type based on their use case.
Microsoft announced a new Foundational C# certification in collaboration with FreeCodeCamp. The Foundational C# certification is completely free for individuals in all regions. The certification includes a 35-hour C# training course hosted on Microsoft Learn and requires completing an 80-question C# certification exam. It's very interesting for those who want to start developing programming skills in C#. This is a great choice, as after completing this certification, you will acquire all the essential C# programming skills and learn tips and tricks.
Without further delay, let's get started, and I will guide you through the certification process step by step.
Create an Account in the Microsoft Learn:
Complete C# Self Learning Course in Microsoft Learn:
Step 3: Complete the training content on Microsoft Learn. (Note: If you have previously completed the training content, you do not need to redo it)
Unable to Verify Trophy in Microsoft Platform :
In this article, we will discuss Microsoft introduced new types DateOnly and TimeOnly in C# and examine the unique functionalities offered by each, and assist in determining the suitable choice for different scenarios.
DateOnly and TimeOnly are value types introduced in .Net 6. These types are part of the .net Date and Time and offer unique advantages over using traditional DateTime for specific use cases that require working with only dates or times without the associated time zone information.%20(1).png "DateOnly And TimeOnly Types In -.NET Core")
Use DateOnly when you need to work with dates exclusively, without any time information. Examples of this include handling birthdays, anniversaries, and event dates.
For example, if you were to gather DOB from users, the optimum data type would be Date only as you wouldn't need timezone information and would only need the date, year, and month.
Use TimeOnly when you require time values without any date information. Examples include representing opening/closing times, countdowns, and scheduling alarms.
In C#, DateTime is a value type and commonly used in all the programming languages, You should use DateTime when you need to work with both date and time components together or when you require time zone information.
DateTime provides a complete representation of a specific point in time, combining both date and time information along with an optional time zone offset. here you can find some use cases where DateTime is more appropriate than using DateOnly or TimeOnly
Hope this article will help you, the choice between DateTime, DateOnly, and TimeOnly should be driven by the specific needs of your application.
If your application primarily deals with only dates or times, and you don't require time zone support or date and time together, then DateOnly and TimeOnly can be more appropriate for improved code clarity and safety. However, if you need to represent points in time with both date and time information, DateTime is the suitable choice.
If you have used Microsoft AI tools in the past, you might remember popular services like Cognitive Services and the Bot Framework. These to...
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