When you create a web app, Azure assigns the app to a subdomain of azurewebsites.net. Suppose your web app is named contoso. Azure creates a URL for your web app as contoso.azurewebsites.net. Azure also assigns a virtual IP address for your app. For a production web app, you might want users to see a custom domain name.
What is a custom domain?
A domain name is the address people type into a web browser to reach your website. A custom domain is a domain name that you own and configure to point to your Azure-hosted app, replacing the default Azure domain.
You can use the Web Apps, Mobile Apps, or API Apps features of Azure App Service, and create your own apps in the Azure portal.
Things to know about configuration settings
Let’s examine some of the basic configuration settings you need to create an app with App Service.
Name: The name for your app must be unique. The name identifies and locates your app in Azure. An example name is webappces1.azurewebsites.net. You can map a custom domain name, if you prefer to use that option instead.
Publish: App Service hosts (publishes) your app as code or as a Docker Container.
Runtime stack: App Service uses a software stack to run your app, including the language and SDK versions. For Linux apps and custom container apps, you can set an optional start-up command or file. Your choices for the stack include .NET Core, .NET Framework, Node.js, PHP, Python, and Ruby. Various versions of each product are available for Linux and Windows.
Operating system: The operating system for your app runtime stack can be Linux or Windows.
Region: The region location that you choose for your app affects the App Service plans that are available.
Pricing plans: Your app needs to be associated with an Azure App Service plan to establish available resources, features, and capacity. You can choose from pricing tiers that are available for the region location you selected.
To classify initiatives into horizons, we first map company initiatives to a prioritization grid. Then we are able to prioritize investments into horizons based on where the initiative falls in the grid.
Map initiatives to a prioritization grid
Start with a matrix with four quadrants that organizes planned initiatives by strategic impact on one axis and business model impact on the other.
The matrix’s horizontal axis represents a spectrum of “tactical” to “strategic” initiatives. “Tactical” initiatives are confined to a single team or use case. “Strategic” initiatives represent larger investments that might affect the entire organization. The matrix’s vertical axis represents a spectrum of business models. Existing business model initiatives address competitive and disruptive threats, improve operations, or empower employees. New business model initiatives create new value propositions and revenue streams.
As you map initiatives, it’s helpful to involve the Chief Financial Officer (CFO) office and other stakeholders to ensure you’ve made the right assumptions around the opportunity valuation.
Let’s try filling in the prioritization grid using the earlier manufacturing example. You might place automation of quality control in the lower left quadrant. It’s an initiative that digitizes and optimizes an existing business model without requiring systemic changes.
Scenarios that fall below the middle line help the organization survive more than thrive. They might address competitive and disruptive threats, improve operations, or empower employees in the organization. Scenarios above the middle line help companies create new value propositions, revenue streams, or business models.
Once you are done classifying your initiatives on the grid, you can map the quadrants to horizons. The quadrant that an initiative fits determines which horizon it belongs to. The initiatives in quadrants one and four belong to Horizon 2. The initiatives in quadrant three belong to Horizon 1. The initiatives in quadrant two belong to Horizon 3.
Prioritize investments based on horizons
We recommend prioritizing initiatives in phases: start with foundational initiatives in the bottom left of the Prioritization framework quadrant and move towards transformational initiatives in the top right of the quadrant.
Having mapped the initiatives to their horizons, you tackle them in order: Horizon 1 initiatives first then Horizon 2 initiatives, and finally Horizon 3 initiatives.
There’s excitement stirring around AI. It’s now clear that AI technologies drive substantial value to organizations and should be embraced to keep a competitive edge. However, the complexity underpinning AI may feel intimidating. Any organization needs a solid plan for AI adoption and scaling to fully benefit from AI’s potential. You should consider AI as a tool to reach your business goals and incorporate it into the corporate strategy.
In Microsoft, we recommend using a holistic framework for AI strategy. This framework applies to all organizations, and provides a sensible approach to AI implementation. This AI strategy framework covers three elements: the external environment that gives you context, the value proposition that you offer to customers, and the executive capabilities of your organization.
External environment
Your starting point should be to understand the external industry environment. Right now, it involves measuring how AI is impacting your sector. This technology is shifting overall buying behavior. AI is leading and empowering new competitors. It’s disrupting current business processes and opening opportunities for new business models. Governments are taking action to deliver new regulations on AI.
During the last decade, we’ve seen the disruptive potential of AI across industries. Now, a new generation of AI models is taking this power to the next level. Generative AI is capable of delivering content and insights with unparalleled results, and this technology changes how we work. Business leaders are already strategizing to implement generative AI to boost productivity. However, keep in mind that AI works best as a copilot, that is, as a guide to help you achieve better results. AI amplifies your expertise and skills.
Value proposition
What do you want to offer your customers? You must consider the benefits and functionalities that your AI-powered products and services will deliver to your clients. There may be opportunities to improve their customer experience by improving a service or by adding new features. AI may help you be more efficient and, allowing you to deliver your solution at a more competitive price. Perhaps it’s time to embrace new business lines opened up by AI. When writing your value proposition, be realistic and take into account costs of production and delivery, since they have a direct impact on the customer experience. The overall goal is to decide how to meet external challenges and leverage key opportunities.
A successful AI strategy must consider cultural issues as well as business issues. Becoming an AI-ready organization requires a fundamental transformation in how you do things, how employees relate to each other, what skills they have, and what processes and principles guide your behaviors. This transformation goes to the core of an organization’s culture, and it’s vital for organizations to tackle such transformation with a holistic approach. Leaders should back this cultural change for everyone at the organization to embrace and adopt AI.
Fostering an AI-ready culture requires:
Being a data-driven organization.
Empowering people to participate in the AI transformation, and creating an inclusive environment that allows cross-functional, multidisciplinary collaboration.
Creating a responsible approach to AI that addresses the challenging questions AI presents.
There’s excitement stirring around AI. It’s now clear that AI technologies drive substantial value to organizations and should be embraced to keep a competitive edge. However, the complexity underpinning AI may feel intimidating. Any organization needs a solid plan for AI adoption and scaling to fully benefit from AI’s potential. You should consider AI as a tool to reach your business goals and incorporate it into the corporate strategy.
In Microsoft, we recommend using a holistic framework for AI strategy. This framework applies to all organizations, and provides a sensible approach to AI implementation. This AI strategy framework covers three elements: the external environment that gives you context, the value proposition that you offer to customers, and the executive capabilities of your organization.
External environment
Your starting point should be to understand the external industry environment. Right now, it involves measuring how AI is impacting your sector. This technology is shifting overall buying behavior. AI is leading and empowering new competitors. It’s disrupting current business processes and opening opportunities for new business models. Governments are taking action to deliver new regulations on AI.
During the last decade, we’ve seen the disruptive potential of AI across industries. Now, a new generation of AI models is taking this power to the next level. Generative AI is capable of delivering content and insights with unparalleled results, and this technology changes how we work. Business leaders are already strategizing to implement generative AI to boost productivity. However, keep in mind that AI works best as a copilot, that is, as a guide to help you achieve better results. AI amplifies your expertise and skills.
Value proposition
What do you want to offer your customers? You must consider the benefits and functionalities that your AI-powered products and services will deliver to your clients. There may be opportunities to improve their customer experience by improving a service or by adding new features. AI may help you be more efficient and, allowing you to deliver your solution at a more competitive price. Perhaps it’s time to embrace new business lines opened up by AI. When writing your value proposition, be realistic and take into account costs of production and delivery, since they have a direct impact on the customer experience. The overall goal is to decide how to meet external challenges and leverage key opportunities.
AI embedded in everyday applications may not be enough to power the business applications an organization needs. In these cases, Power Platform is the next step towards more customizable AI solutions. It provides a simple, low-code way to introduce AI in your business applications without having to create or manage the AI yourself.
What is Microsoft Power Platform?
Microsoft Power Platform provides low-code and no-code services designed to simplify the process of building technical solutions. It provides building blocks that help teams work faster. Even if Power Platform isn’t centered on AI, its services are often powered by AI and help you create smart solutions.
The Power Platform portfolio includes five different products: Power BI, Power Apps, Power Automate, Copilot Studio, and Power Pages. It also offers three additional tools: AI Builder, Microsoft Dataverse, and Connectors. Let’s see what each of them can do for you.
The availability of sophisticated AI models can help organizations reduce significantly the intimidating amount of resources a data science project can require. Let’s see how organizations can tackle machine learning challenges and operations with Azure Machine Learning.
Machine learning challenges and machine learning operations
Maintaining AI solutions typically requires machine learning lifecycle management to document and manage data, code, model environments, and the machine learning models themselves. You need to establish processes for developing, packaging, and deploying models, as well as monitoring their performance and occasionally retraining them. And most organizations are managing multiple models in production at the same time, adding to the complexity.
To cope effectively with this complexity, some best practices are required. They focus on cross-team collaboration, automating and standardizing processes, and ensuring models can be easily audited, explained, and reused. To get this done, data science teams rely on the machine learning operations approach. This methodology is inspired by DevOps (development and operations), the industry standard for managing operations for an application development cycle, since the struggles of developers and data scientists are similar.
This unit discusses the prebuilt AI models that are available in Azure AI Services. They are a solid alternative to developing internal custom AI models.
What is Azure AI Services?
When considering adopting AI into your business, you should consider prebuilt AI services first. Azure AI Services is a Microsoft product that delivers AI as SaaS. It includes pretrained models developed by Microsoft global researchers and data scientists to solve common problems. To avoid reinventing the wheel, businesses can leverage prebuilt services to achieve quality and accelerate delivery of technology solutions.
It’s better to use the Azure AI Services that offer prebuilt AI services in vision, speech, language, search, or generative AI to solve common scenarios. This brings AI within reach of every developer and organization without requiring machine learning expertise. As a result, it enables developers of all skill levels to easily add intelligence to new or existing business applications.
Using Azure AI Services can:
Save costs: Since AI Services is serverless, they’re usually less costly than developing and training custom models from scratch internally.
Give deployment flexibility: You can export AI Services models and run them wherever you need, in the cloud, on-premises, or on the edge.
Provide enterprise-level security: AI services provide a layered security model, including authentication with Microsoft Entra credentials, a valid resource key, and Azure Virtual Networks.
Connect to an ecosystem of products: AI services are part of a broad ecosystem that includes automation and integration tools, deployment options, Docker containers for secure access, and tools for big data scenarios.
AI is disrupting every industry and every business. For the last decade, AI has enabled companies of all sizes to achieve better business results. There’s already a mainstream business use of AI thanks to these three trends:
Access to massive amounts of data.
Access to massive computing power through the cloud.
Access to AI algorithms.
AI is experiencing major breakthroughs. A new generation of Large Language Models (LLMs) enables new use cases that weren’t possible a few years ago, such as those based on high-quality generative AI. Based on these technologies, organizations will experience a second wave of AI-powered transformation. However, businesses need an easy way to access the latest AI capabilities to take full advantage of them.
Microsoft is working to democratize AI use. It has designed a wide range of solutions and services to bring AI to everyone, irrespective of their level of AI expertise. There are four approaches, varying based on the level of AI and coding expertise required.
