Azure AI services include a wide range of pre-defined tools, frameworks, and templates designed to enable the development, deployment, and management of AI-based applications. These services leverage machine learning (ML), natural language processing (NLP), computer vision, and other artificial intelligence techniques to solve complex business problems. In this article we will see what are the main services offered, how they work, in what contexts they can help us and what are the costs to exploit the power of AI within Microsoft's cloud computing platform.
In a constantly evolving technological ecosystem, Artificial Intelligence has emerged as a turning point in many areas and cloud platforms have played a fundamental role in its diffusion in the mainstream digital landscape and in exploiting its potential to create intelligent solutions for a wide variety of businesses and sectors.
Among the pioneers of this technological revolution, Microsoft's Azure AI services immediately stood out, offering within the Redmond cloud computing platform a suite of tools designed to empower developers and companies in the creation of intelligent, market-ready applications with a high level of responsiveness, with pre-defined, ready-to-use tools, APIs and templates.
Incorporating Azure AI services into your projects opens up a world of possibilities, whether you're in healthcare, finance, retail, manufacturing, or any other industry. The ability to customize and refine models, combined with the scalability and security offered by Azure, makes this platform an ideal choice for companies that want to leverage AI to gain a competitive advantage.
From machine learning to natural language processing, to (among the many other options available) to artificial vision that allows you to extract information from images and non-text files, Azure's AI tools could be what your business is looking for for its technological leap in quality. Let's take a closer look at it.
When Microsoft does things, it tends to want to do it big, and the number of Azure services is a case in point. The cloud platform offers more than 200 of the most disparate services, with more than a dozen dedicated exclusively to AI and the possibility of integrating artificial intelligence functionality into others.
And the number continues to increase as time goes by, with Microsoft continuing to update its cloud computing environment with new services, the restructuring and expansion of old ones and, in the specific case of AI, with the addition of support for new models. At Microsoft Build 2024 last April, the Redmond company confirmed that the models available for Azure AI Studio will exceed the dizzying figure of more than 1600!
Describing down to the smallest detail each of the services available in an article would require writing the equivalent of a wiki. We will therefore limit ourselves in this section to a general overview of the artificial intelligence services provided by Azure and we will see in broad strokes their operation and the possible common use cases for everyone.
We have created the Infrastructure & Security team, focused on the Azure cloud, to better respond to the needs of our customers who involve us in technical and strategic decisions. In addition to configuring and managing the tenant, we also take care of:
With Dev4Side, you have a reliable partner that supports you across the entire Microsoft application ecosystem.
Anomaly Detector is an Azure AI service that allows developers to monitor and detect anomalies in their time series data without requiring advanced machine learning knowledge. It can detect peaks, dips, deviations from cyclical models, and trend changes through univariate (a single variable) and multivariate (multiple variables) APIs. The service can be customized to detect different levels of anomaly and can be distributed in the cloud or near the point of origin of the data (what in computer science is known as the intelligent edge).
The Anomaly Detector API provides two methods of detecting anomalies: batch and streaming. Developers can use the batch detection method to detect anomalies in a batch of data points over a certain amount of time, while the streaming method allows them to monitor data in real time by sending new data points as they are generated. The underlying AI models are trained and customized using user data, allowing the service to understand the unique needs of their business.
The service is ideal for ad-hoc data analysis and can be used in other platforms or applications such as Power BI, Azure Data Explorer and Azure Synapse.
Azure OpenAI is a service provided by Microsoft in collaboration with OpenAI that allows companies and developers to integrate advanced artificial intelligence models into their applications through the Azure cloud infrastructure.
At the heart of the service are GPT (Generative Pre-trained Transformers) models, designed to process and generate text in natural language. These models use a Transformer-type neural network architecture, which stands out for its ability to manage large text sequences through attention mechanisms.
These mechanisms allow the model to focus on specific parts of the text, significantly improving contextual understanding and the quality of language generation. GPT-4, one of the best known of these models, is pre-trained on huge amounts of textual data and can be further trained on specific datasets to adapt to more particular tasks.
Azure OpenAI can be used to develop a wide range of solutions and applications, including text generation, natural language analysis, machine translation, chatbot creation, and much more.
Azure AI Vision is a suite of pre-trained machine learning models from Microsoft, designed to help developers easily add intelligent functionality to their applications.
Using Azure Vision Studio, a set of UI-based tools that allows you to explore, build and integrate functionality from Azure AI Vision, you can start experimenting with services and understand what they offer without the need to write any code.
You can then use the available client libraries and REST APIs to integrate these services into your applications. Azure AI Vision offers several functionalities that use pre-built and pre-configured models to perform various tasks, such as understanding the movement of people through a space, detecting faces in images and extracting text from images.
The Azure Computer Vision service can analyze and understand the contents of images. It can identify objects, people, text, scenes, and activities in images, as well as detect any inappropriate content.
Custom Vision, on the other hand, allows developers to create, distribute and improve their image classifiers and allows them to train their model using customized images and labels.
The Custom Vision service uses a machine learning algorithm to analyze images. Series of images that have or do not have the desired visual characteristics are sent. Next, you label the images with your own personalized labels (tags) at the time of submission. The algorithm trains on this data and calculates its accuracy by testing itself on the same images.
Once the model has been trained, it can be tested, retrained and finally used in the image recognition app to classify images or detect objects. You can also export the template for offline use.
Azure AI Search (also known as Azure Cognitive Search) is a cloud-based search service (Search as a Service or SaaS) that provides programmers with the infrastructure, APIs and tools necessary to create advanced search experiences from private and heterogeneous data collections and then exploit them in web applications, mobile apps and business solutions.
The service provides two different indexing engines: Microsoft's proprietary Natural Language Processing (NLP) technology or the Apache Lucene open source library analyzers.
The service can use optical character recognition (OCR) to extract text from scanned images or documents without the need for additional intervention. The use of machine learning algorithms also allows Cognitive Search to understand the meaning of the text and gives users the opportunity to find information even if a document does not contain exactly the keyword they were looking for.
Azure Cognitive Search could therefore still find the information we want to obtain taking into account the contextual proximity to the topic searched, the synonyms of the keywords used in the indexed results and other discriminating criteria to narrow down the relevant results without sacrificing tangentiality and potentially significant correlations.
Azure AI Speech is a service that offers cutting-edge speech capabilities such as voice-to-text, text-to-speech, voice translation, and speaker recognition.
The service allows developers to quickly develop high-quality voice functionality for apps and create voice assistants with customizable voices and templates whose vocabulary can be expanded to specialize them however they prefer. But not only that, Speech in fact allows developers to build their own models.
Azure AI Speech is extremely flexible and can be distributed anywhere and used to transcribe audio in more than 100 languages and variants, obtain customer insights with call center transcription, improve experiences with voice assistants and capture key discussions in meetings.
Azure Machine Learning is a platform that not only facilitates the creation, training and deployment of large scale machine learning models, but also offers advanced tools for managing and monitoring these models once implemented.
Through its intuitive interface and automation capabilities, Azure Machine Learning simplifies collaboration between data scientists and developers, allowing them to explore data, develop predictive models and optimize performance without having to manage the underlying infrastructure.
Azure integrated services, such as Azure Notebooks and Azure DevOps, complete the ecosystem by offering an integrated workflow for experimenting, testing and putting models into production quickly and efficiently.
Bot Service allows developers to create, connect, deploy and manage intelligent chatbots. These bots are designed to interact with users naturally and conversationally through a variety of communication channels.
Through integration with cognitive services such as Azure Cognitive Services, bots can understand and respond to natural language, analyze user sentiment and provide contextual and personalized answers.
In addition, Azure Bot Service supports a large number of communication channels, including web platforms, mobile applications and collaboration tools such as Microsoft Teams, thus expanding the reach and accessibility of bots to improve the user experience in various contexts.
Azure AI Content Moderator is an AI service that allows users to manage potentially offensive, risky, or otherwise unwanted content. The service includes content moderation powered by artificial intelligence that automatically analyzes texts, images and videos, reporting and flagging content deemed inappropriate.
Content Moderator is a comprehensive solution designed to detect harmful user-generated and AI-generated content in applications and services and is suitable for multiple scenarios, such as online marketplaces, game companies, social messaging platforms, enterprise media companies, and providers of educational solutions for education. The service is available through REST API and client library SDK in all of the most popular development languages at the moment.
Azure AI Document Intelligence is an automated data processing system that uses artificial intelligence and OCR and applies advanced machine learning to extract texts, key-value pairs, tables and structures from documents automatically and precisely.
The service then transforms documents into usable data and shifts the focus to the action of the information rather than its compilation and can be used to develop intelligent document processing solutions. Users can start with predefined templates or create custom templates adapted to their documents both locally and in the cloud with the AI Document Intelligence studio or SDK.
Azure AI Video Indexer is a cloud application that uses Azure Media Services and Azure AI services to extract information from video and audio content running more than 30 artificial intelligence models and generate detailed insights.
Azure AI Video Indexer insights can be applied to multiple scenarios, including content creation, advertising optimization, and management of digital assets and media libraries. The service is accessible through an SDK client library, REST API or through the Azure AI Video Indexer web portal.
Azure AI Personalizer is an artificial intelligence service that helps applications make smarter decisions at scale using reinforcement learning.
The service processes information on the state of the application, the context and/or the users (contexts), and a set of possible decisions and related attributes (actions) to determine the best decision to make.
Feedback from the application (reward) is sent to Personalizer to learn how to improve its ability to make decisions in near real time.
The service is accessible through an SDK client library, REST API, or through the Personalizer web portal.
Azure AI Metrics Advisor is a time series monitoring platform that provides a set of APIs to automate the process of applying models to data and a web-based workspace for data ingestion, anomaly detection and diagnostics without the need for machine learning knowledge.
Metrics Advisor can connect to and ingest multidimensional metric data from many data storage architectures, including SQL Server, Azure Blob Storage, and MongoDB.
Azure AI Immersive Reader is an artificial intelligence service that helps users read and understand text with tools and features such as reading aloud, language translation and focusing attention through highlights and other design elements.
The main purpose of the service is to make reading easier and more accessible for everyone, including new readers, language learners and people with learning disorders such as dyslexia, dysgraphia and dyscalculia.
Azure is the only major cloud provider that currently offers this type of text reading technology, making it one of the most inclusive platforms for people with ASD.
For AI services provided through Azure, Microsoft provides different pricing models to adapt to the needs of developers and organizations. The costs may vary based on factors such as, for example, the type of service used, the volumes of use and the region in which the service is distributed.
Microsoft uses London's closing exchange rates to determine changes in currencies other than the US dollar. The prices listed on the official Azure website are estimates and actual costs may also vary based on the specific agreement with Microsoft, the date of purchase and other factors.
Each service therefore has variations in prices and in their calculation, but there are characteristics common to all their pricing models that we will list below to get a clearer idea of how they work in general:
To obtain a more precise estimate of the costs for using Azure AI services, it is advisable to use the Azure pricing calculator available on the official website, which allows you to enter specific project details to obtain a cost forecast.
Incorporating Azure AI services into your projects opens up a world of possibilities, whether you operate in the financial sector, retail, manufacturing or, really, in any other sector. The ability to customize and optimize models, combined with the scalability and security offered by Azure, makes it an ideal choice for companies looking to exploit AI to gain a competitive advantage in the constantly evolving landscape of modern businesses.
Azure AI services are more than just tools and represent a future in which the creation of intelligent and cutting-edge applications that revolutionize the way we interact with technology is no longer a science-fiction scenario.
By adopting these services, developers and organizations can not only stay at the forefront of the technology landscape, but also help create a smarter and more inclusive digital future.
So why not try it?
Azure AI is a collection of AI services and tools provided by Microsoft Azure that enables businesses to integrate machine learning and artificial intelligence capabilities into their applications and workflows.
Azure AI includes services such as Azure Machine Learning, Azure Cognitive Services, Azure Bot Service, and Azure OpenAI Service, all designed to help developers build, train, and deploy AI models efficiently.
Azure AI supports machine learning through Azure Machine Learning, a service that provides a comprehensive environment for building, training, and deploying machine learning models at scale.
Azure Cognitive Services are a part of Azure AI, offering pre-built APIs for tasks like vision, speech, language understanding, and decision-making, enabling developers to integrate AI capabilities into their applications without needing deep AI expertise.
You can integrate natural language processing (NLP) with Azure AI using Azure Cognitive Services, specifically the Language service, which offers capabilities like sentiment analysis, language detection, and text translation.
The Azure OpenAI Service is a component of Azure AI that allows you to access advanced language models, such as GPT, to generate human-like text, enabling new possibilities for content generation, customer support, and more.
Azure AI enhances customer interactions through services like Azure Bot Service, which allows you to build, deploy, and manage intelligent chatbots that can handle a variety of customer queries and tasks automatically.
Yes, Azure AI allows you to customize models, especially with Azure Machine Learning, where you can train custom models using your data to meet specific business needs.
Azure AI ensures data security by adhering to Azure’s comprehensive set of security features, including encryption, identity management, and compliance with various international standards.
Yes, Azure AI is designed to be accessible to both developers and non-developers, with tools like Azure Cognitive Services providing user-friendly APIs that do not require extensive AI or coding knowledge.
The Infra & Security team focuses on the management and evolution of our customers' Microsoft Azure tenants. Besides configuring and managing these tenants, the team is responsible for creating application deployments through DevOps pipelines. It also monitors and manages all security aspects of the tenants and supports Security Operations Centers (SOC).
