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While you can perform many of the tasks needed to develop an AI solution directly in the Azure AI Foundry portal, developers also need to write, test, and deploy code.

Development tools and environments

There are many development tools and environments available, and developers should choose one that supports the languages, SDKs, and APIs they need to work with and with which they’re most comfortable. For example, a developer who focuses strongly on building applications for Windows using the .NET Framework might prefer to work in an integrated development environment (IDE) like Microsoft Visual Studio. Conversely, a web application developer who works with a wide range of open-source languages and libraries might prefer to use a code editor like Visual Studio Code (VS Code). Both of these products are suitable for developing AI applications on Azure.

The Azure AI Foundry for Visual Studio Code extension

When developing Azure AI Foundry based generative AI applications in Visual Studio Code, you can use the Azure AI Foundry for Visual Studio Code extension to simplify key tasks in the workflow, including:

• Creating a project.
• Selecting and deploying a model.
• Testing a model in the playground.
• Creating an agent.

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Azure AI Foundry is a platform for AI development on Microsoft Azure. While you can provision individual Azure AI services resources and build applications that consume them without it, the project organization, resource management, and AI development capabilities of Azure AI Foundry makes it the recommended way to build all but the most simple solutions.

Azure AI Foundry provides the Azure AI Foundry portal, a web-based visual interface for working with AI projects. It also provides the Azure AI Foundry SDK, which you can use to build AI solutions programmatically.

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Microsoft Azure provides a wide range of cloud services that you can use to develop, deploy, and manage an AI solution. The most obvious starting point for considering AI development on Azure is Azure AI services; a set of out-of-the-box prebuilt APIs and models that you can integrate into your applications. The following table lists some commonly used Azure AI services.

Considerations for Azure AI services resources

To use Azure AI services, you create one or more Azure AI resources in an Azure subscription and implement code in client applications to consume them. In some cases, AI services include web-based visual interfaces that you can use to configure and test your resources – for example to train a custom image classification model using the Custom Vision service you can use the visual interface to upload training images, manage training jobs, and deploy the resulting model.

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The term “Artificial Intelligence” (AI) covers a wide range of software capabilities that enable applications to exhibit human-like behavior. AI has been around for many years, and its definition has varied as the technology and use cases associated with it have evolved. In today’s technological landscape, AI solutions are built on machine learning models that encapsulate semantic relationships found in huge quantities of data; enabling applications to appear to interpret input in various formats, reason over the input data, and generate appropriate responses and predictions.

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In this module, you’ll be introduced to general cloud concepts. You’ll start with an introduction to the cloud in general. Then you’ll dive into concepts like shared responsibility, different cloud models, and explore the unique pricing method for the cloud.

If you’re already familiar with cloud computing, this module may be largely review for you.

Learning objectives

After completing this module, you’ll be able to:

• Define cloud computing.
• Describe the shared responsibility model.
• Define cloud models, including public, private, and hybrid.
• Identify appropriate use cases for each cloud model.
• Describe the consumption-based model.
• Compare cloud pricing models.

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A major benefit of cloud computing is the manageability options. There are two types of manageability for cloud computing that you’ll learn about in this series, and both are excellent benefits.

Management of the cloud

Management of the cloud speaks to managing your cloud resources. In the cloud, you can:

• Automatically scale resource deployment based on need.
• Deploy resources based on a preconfigured template, removing the need for manual configuration.
• Monitor the health of resources and automatically replace failing resources.
• Receive automatic alerts based on configured metrics, so you’re aware of performance in real time.

Management in the cloud

Management in the cloud speaks to how you’re able to manage your cloud environment and resources. You can manage these:

• Through a web portal.
• Using a command line interface.
• Using APIs.
• Using PowerShell.

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Whether you’re deploying infrastructure as a service or software as a service, cloud features support governance and compliance. Things like set templates help ensure that all your deployed resources meet corporate standards and government regulatory requirements. Plus, you can update all your deployed resources to new standards as standards change. Cloud-based auditing helps flag any resource that’s out of compliance with your corporate standards and provides mitigation strategies. Depending on your operating model, software patches and updates may also automatically be applied, which helps with both governance and security.

On the security side, you can find a cloud solution that matches your security needs. If you want maximum control of security, infrastructure as a service provides you with physical resources but lets you manage the operating systems and installed software, including patches and maintenance. If you want patches and maintenance taken care of automatically, platform as a service or software as a service deployments may be the best cloud strategies for you.

And because the cloud is intended as an over-the-internet delivery of IT resources, cloud providers are typically well suited to handle things like distributed denial of service (DDoS) attacks, making your network more robust and secure.

By establishing a good governance footprint early, you can keep your cloud footprint updated, secure, and well managed.

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Reliability and predictability are two crucial cloud benefits that help you develop solutions with confidence.

Reliability

Reliability is the ability of a system to recover from failures and continue to function. It’s also one of the pillars of the Microsoft Azure Well-Architected Framework.

The cloud, by virtue of its decentralized design, naturally supports a reliable and resilient infrastructure. With a decentralized design, the cloud enables you to have resources deployed in regions around the world. With this global scale, even if one region has a catastrophic event other regions are still up and running. You can design your applications to automatically take advantage of this increased reliability. In some cases, your cloud environment itself will automatically shift to a different region for you, with no action needed on your part. You’ll learn more about how Azure leverages global scale to provide reliability later in this series.

Predictability

Predictability in the cloud lets you move forward with confidence. Predictability can be focused on performance predictability or cost predictability. Both performance and cost predictability are heavily influenced by the Microsoft Azure Well-Architected Framework. Deploy a solution built around this framework and you have a solution whose cost and performance are predictable.

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When building or deploying a cloud application, two of the biggest considerations are uptime (or availability) and the ability to handle demand (or scale).

High availability

When you’re deploying an application, a service, or any IT resources, it’s important the resources are available when needed. High availability focuses on ensuring maximum availability, regardless of disruptions or events that may occur.

When you’re architecting your solution, you’ll need to account for service availability guarantees. Azure is a highly available cloud environment with uptime guarantees depending on the service. These guarantees are part of the service-level agreements (SLAs).

Scalability

Another major benefit of cloud computing is the scalability of cloud resources. Scalability refers to the ability to adjust resources to meet demand. If you suddenly experience peak traffic and your systems are overwhelmed, the ability to scale means you can add more resources to better handle the increased demand.

The other benefit of scalability is that you aren’t overpaying for services. Because the cloud is a consumption-based model, you only pay for what you use. If demand drops off, you can reduce your resources and thereby reduce your costs.

Scaling generally comes in two varieties: vertical and horizontal. Vertical scaling is focused on increasing or decreasing the capabilities of resources. Horizontal scaling is adding or subtracting the number of resources.

Vertical scaling

With vertical scaling, if you were developing an app and you needed more processing power, you could vertically scale up to add more CPUs or RAM to the virtual machine. Conversely, if you realized you had over-specified the needs, you could vertically scale down by lowering the CPU or RAM specifications.

Horizontal scaling

With horizontal scaling, if you suddenly experienced a steep jump in demand, your deployed resources could be scaled out (either automatically or manually). For example, you could add additional virtual machines or containers, scaling out. In the same manner, if there was a significant drop in demand, deployed resources could be scaled in (either automatically or manually), scaling in.

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When comparing IT infrastructure models, there are two types of expenses to consider. Capital expenditure (CapEx) and operational expenditure (OpEx).

CapEx is typically a one-time, up-front expenditure to purchase or secure tangible resources. A new building, repaving the parking lot, building a datacenter, or buying a company vehicle are examples of CapEx.

In contrast, OpEx is spending money on services or products over time. Renting a convention center, leasing a company vehicle, or signing up for cloud services are all examples of OpEx.

Cloud computing falls under OpEx because cloud computing operates on a consumption-based model. With cloud computing, you don’t pay for the physical infrastructure, the electricity, the security, or anything else associated with maintaining a datacenter. Instead, you pay for the IT resources you use. If you don’t use any IT resources this month, you don’t pay for any IT resources.

This consumption-based model has many benefits, including:

• No upfront costs.
• No need to purchase and manage costly infrastructure that users might not use to its fullest potential.
• The ability to pay for more resources when they’re needed.
• The ability to stop paying for resources that are no longer needed.

With a traditional datacenter, you try to estimate the future resource needs. If you overestimate, you spend more on your datacenter than you need to and potentially waste money. If you underestimate, your datacenter will quickly reach capacity and your applications and services may suffer from decreased performance. Fixing an under-provisioned datacenter can take a long time. You may need to order, receive, and install more hardware. You’ll also need to add power, cooling, and networking for the extra hardware.

In a cloud-based model, you don’t have to worry about getting the resource needs just right. If you find that you need more virtual machines, you add more. If the demand drops and you don’t need as many virtual machines, you remove machines as needed. Either way, you’re only paying for the virtual machines that you use, not the “extra capacity” that the cloud provider has on hand.

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