Edit

Share via

Artificial intelligence (AI) architecture design

  • Article

Artificial intelligence (AI) is a technology that allows machines to imitate intelligent human behavior. With AI, machines can:

  • Analyze data to create images and videos.
  • Analyze and synthesize speech.
  • Verbally interact in natural ways.
  • Make predictions and generate new data.

Architects design workloads that use artificial intelligence to perform functions or make decisions where traditional logic or processing would be prohibitive, or even nearly impossible, to implement. As an architect designing a solution, it's important to understand the AI and machine learning landscape and how Azure offers solutions to integration into your workload design.

AI concepts

Algorithms

Algorithms or machine learning algorithms are pieces of code that help humans explore, analyze, and find meaning in complex data sets. Each algorithm is a finite set of unambiguous step-by-step instructions that a machine can follow to achieve a certain goal. In a machine learning model, the goal is to establish or discover patterns that humans can use to make predictions or categorize information. An algorithm may describe how to determine whether a pet is a cat, dog, fish, bird, or lizard. Another far more complicated algorithm may describe how to identify a written or spoken language, analyze its words, translate them into a different language, and then check the translation for accuracy.

When you design a workload, you'll need to select an algorithm family that is suited for your task and evaluate the various available algorithms to find the appropriate fit.

Machine learning

Machine learning is an AI technique that uses algorithms to create predictive models. The algorithm is used to parse data fields and to "learn" from that data by using patterns found within it to generate models. Those models are then used to make informed predictions or decisions about new data.

The predictive models are validated against known data, measured by performance metrics selected for specific business scenarios, and then adjusted as needed. This process of learning and validation is called training. Through periodic retraining, ML models are improved over time.

When it comes to workload design, you consider using machine learning when you have a situation where past observations can reliably be used to predict future situations. These observations can be universal truths such as computer vision that detects one form of animal from another, or these observations can be specific to your situation such as computer vision that detects a potential assembly mistake on your assembly lines based on past warranty claim data.

Deep learning

Deep learning is a type of ML that can learn through its own data processing. Like machine learning, it also uses algorithms to analyze data, but it does by using artificial neural networks that contains many inputs, outputs, and layers of processing. Each layer can process the data in a different way, and the output of one layer becomes the input for the next. This allows deep learning to create more complex models than traditional machine learning.

As a workload designer, this option requires a large investment in generating highly customized or exploratory models. Generally speaking, you'll consider other solutions presented in this article before adding deep learning into your workload.

Generative AI

Generative AI is a form of artificial intelligence in which models are trained to generate new original content based on many forms of content such as natural language, computer vision, audio, or image input. With generative AI, you can describe a desired output in normal everyday language, and the model can respond by creating appropriate text, image, code, and more. Some examples of generative AI applications are:

  • Microsoft Copilot is primarily a user interface that can assist users in writing code, documents, and other text-based content. It's based on popular OpenAI models and is integrated into a wide range of Microsoft applications and user experiences.

  • Azure OpenAI is a development platform as a service that provides access to OpenAI's powerful language models such as o1-preview, o1-mini, GPT-4o, GPT-4o mini, GPT-4 Turbo with Vision, GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be adapted to your specific task such as:

    • Content generation
    • Content summarization
    • Image understanding
    • Semantic search
    • Natural language to code translation.

Language models

Language models are a subset of Generative AI that focuses on natural language processing (NLP) tasks, such as text generation and sentiment analysis. These models represent natural language based on the probability of words or sequences of words occurring in a given context.

Conventional language models have used in supervised settings for research purposes where the models are trained on well-labeled text datasets for specific tasks. Pretrained language models offer an accessible way to get started with AI and have become more widely used in recent years. These models are trained on large-scale text corpora from the internet using deep learning neural networks and can be fine-tuned on smaller datasets for specific tasks.

The size of a language model is determined by its number of parameters, or weights, that determine how the model processes input data and generates output. Parameters are learned during the training process by adjusting the weights within layers of the model to minimize the difference between the model's predictions and the actual data. The more parameters a model has, the more complex and expressive it is, but also the more computationally expensive it is to train and use.

In general, small language models have fewer than 10 billion parameters, and large language models have more than 10 billion parameters. For example, the Microsoft Phi-3 model family has three versions with different sizes: mini (3.8 billion parameters), small (7 billion parameters), and medium (14 billion parameters).

Copilots

The availability of language models led to the emergence of new ways to interact with applications and systems through digital copilots and connected, domain specific, agents. Copilots are generative AI assistants that are integrated into applications often as chat interfaces. They provide contextualized support for common tasks in those applications.

Microsoft Copilot is integrated into a wide range of Microsoft applications and user experiences. It's based on an open architecture that enables third-party developers to create their own plug-ins to extend or customize the user experience with Microsoft Copilot. Additionally, third-party developers can create their own copilots using the same open architecture.

Retrieval Augmented Generation (RAG)

Retrieval Augmented Generation (RAG) is an architecture pattern that augments the capabilities of a large language model (LLM) like ChatGPT, that was trained only on public data. This pattern allows you to add a retrieval system that provides relevant grounding data in the context with the user request. Adding an information retrieval system gives you control over grounding data used by a language model when it formulates a response. RAG architecture helps you scope generative AI to content that's sourced from vectorized documents, images, and other data formats. RAG is not limited to vector search storage however, the pattern is applicable in conjunction with any data store technology.

Automated machine learning (AutoML)

Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.

AI services

With Azure AI services developers and organizations can create intelligent, market-ready, and responsible applications with out-of-the-box, prebuilt and customizable APIs and models. Usages include natural language processing for conversations, search, monitoring, translation, speech, vision, and decision-making.

AI Language models

  • Large Language Models (LLMs), such as OpenAI's GPT models, are powerful tools that can generate natural language across various domains and tasks. When considering using these models, consider factors such as data privacy, ethical use, accuracy, and bias.

  • Phi open models are small, less compute-intensive models for generative AI solutions. A small language model (SLM) may be more efficient, interpretable, and explainable than a large language model.

When designing a workload, you can use language models both as a hosted solution, behind a metered API or for many small language models you can host those in process or at least on the same compute as the consumer. When using language models in your solution, consider your choice of language model and its available hosting options to ensure your using an optimized solution for your use case.

AI development platforms and tools

Azure Machine Learning service

Azure Machine Learning is a machine learning service to build and deploy models. Azure Machine Learning offers web interfaces and SDKs so you can train and deploy your machine learning models and pipelines at scale. Use these capabilities with open-source Python frameworks, such as PyTorch, TensorFlow, and scikit-learn.

Machine learning reference architectures for Azure

Automated machine learning (AutoML)

Build ML models at scale using the AutoML capability in Azure Machine Learning to automate tasks.

MLflow

Azure Machine Learning workspaces are MLflow-compatible, which means that you can use an Azure Machine Learning workspace the same way you use an MLflow server. This compatibility has the following advantages:

  • Azure Machine Learning doesn't host MLflow server instances, but can use the MLflow APIs directly.
  • You can use an Azure Machine Learning workspace as your tracking server for any MLflow code, whether or not it runs in Azure Machine Learning. You only need to configure MLflow to point to the workspace where the tracking should occur.
  • You can run any training routine that uses MLflow in Azure Machine Learning without making any changes.

For more information, see MLflow and Azure Machine Learning

Generative AI tools

  • Prompt flow is a suite of development tools designed to streamline the end-to-end development cycle of generative AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It supports prompt engineering through expressing actions in a modular orchestration and flow engine.

  • Azure AI Studio helps you experiment, develop, and deploy generative AI apps and APIs responsibly with a comprehensive platform. With Azure AI Studio, you have access to Azure AI services, foundation models, playground, and resources to help you build, train, fine-tune, and deploy AI models. Also, you can evaluate model responses and orchestrate prompt application components with prompt flow for better performance.

  • Azure Copilot Studio is used to extend Microsoft Copilot in Microsoft 365 and build custom copilots for internal and external scenarios. With Copilot Studio, users can design, test, and publish copilots using the comprehensive authoring canvas. Users can easily create generative AI-enabled conversations, provide greater control to responses for existing copilots, and accelerate productivity with specific automated workflows.

Data platforms for AI

Microsoft Fabric

Microsoft Fabric is an end-to-end analytics and data platform designed for enterprises that require a unified solution. Workload teams can be granted access to data in these systems. It encompasses data movement, processing, ingestion, transformation, real-time event routing, and report building. It offers a comprehensive suite of services including Data Engineering, Data Factory, Data Science, Real-Time Analytics, Data Warehouse, and Databases.

Microsoft Fabric integrates separate components into a cohesive stack. Instead of relying on different databases or data warehouses, you can centralize data storage with OneLake. AI capabilities are embedded within Fabric, eliminating the need for manual integration.

Copilots in Fabric

Copilot and other generative AI features let you transform and analyze data, generate insights, and create visualizations and reports in Microsoft Fabric and Power BI. You can either build your own copilot, or choose one of the following prebuilt copilots:

AI skills in Fabric

With a Microsoft Fabric AI skill, you can configure a generative AI system to generate queries that answer questions about your data. After you configure the AI skill, you can share it with your colleagues, who can then ask their questions in plain English. Based on their questions, the AI generates queries over your data that answer those questions.

Apache Spark-based data platforms for AI

Apache Spark is a parallel processing framework that supports in-memory processing to boost the performance of big data analytic applications. Spark provides primitives for in-memory cluster computing. A Spark job can load and cache data into memory and query it repeatedly, which is faster than disk-based applications, such as Hadoop.

Apache Spark in Azure Fabric

Microsoft Fabric Runtime is an Azure-integrated platform based on Apache Spark that enables the execution and management of data engineering and data science experiences. It combines key components from both internal and open-source sources, providing customers with a comprehensive solution.

Major components of Fabric Runtime:

  • Apache Spark - a powerful open-source distributed computing library that enables large-scale data processing and analytics tasks. Apache Spark provides a versatile and high-performance platform for data engineering and data science experiences.

  • Delta Lake - an open-source storage layer that brings ACID transactions and other data reliability features to Apache Spark. Integrated within Fabric Runtime, Delta Lake enhances data processing capabilities and ensures data consistency across multiple concurrent operations.

  • Default-level packages for Java/Scala, Python, and R - packages that support diverse programming languages and environments. These packages are automatically installed and configured, allowing developers to apply their preferred programming languages for data processing tasks.

The Microsoft Fabric Runtime is built upon a robust open-source operating system, ensuring compatibility with various hardware configurations and system requirements.

Azure Databricks Runtime for Machine Learning

Azure Databricks is an Apache Spark–based analytics platform with one-click setup, streamlined workflows, and an interactive workspace for collaboration between data scientists, engineers, and business analysts.

Databricks Runtime for Machine Learning (Databricks Runtime ML) lets you start a Databricks cluster with all of the libraries required for distributed training. It provides an environment for machine learning and data science. Plus, it contains multiple popular libraries, including TensorFlow, PyTorch, Keras, and XGBoost. It also supports distributed training using Horovod.

Apache Spark in Azure HDInsight

Apache Spark in Azure HDInsight is the Microsoft implementation of Apache Spark in the cloud. Spark clusters in HDInsight are compatible with Azure Storage and Azure Data Lake Storage, so you can use HDInsight Spark clusters to process your data stored in Azure.

The Microsoft machine learning library for Apache Spark is SynapseML (formerly known as MMLSpark). This open-source library adds many deep learning and data science tools, networking capabilities, and production-grade performance to the Spark ecosystem. Learn more about SynapseML features and capabilities.

Data storage for AI

Microsoft Fabric OneLake

OneLake in Fabric is a unified and logical data lake that's tailored for the entire organization. It serves as the central hub for all analytics data and is included with every Microsoft Fabric tenant. OneLake in Fabric is built on the foundation of Data Lake Storage Gen2.

OneLake in Fabric:

  • Supports structured and unstructured file types.
  • Stores all tabular data in Delta Parquet format.
  • Provides a single data lake within tenant boundaries that's governed by default.
  • Supports the creation of workspaces within a tenant so that an organization can distribute ownership and access policies.
  • Supports the creation of various data items, such as lakehouses and warehouses, from which you can access data.

For more information, see OneLake, the OneDrive for data.

Azure Data Lake Storage Gen2

Azure Data Lake Storage is a single, centralized repository where you can store all your data, both structured and unstructured. A data lake enables your organization to quickly and more easily store, access, and analyze a wide variety of data in a single location. With a data lake, you don't need to conform your data to fit an existing structure. Instead, you can store your data in its raw or native format, usually as files or as binary large objects (blobs).

Data Lake Storage Gen2 provides file system semantics, file-level security, and scale. Because these capabilities are built on Blob storage, you also get low-cost, tiered storage, with high availability/disaster recovery capabilities.

Data Lake Storage Gen2 makes Azure Storage the foundation for building enterprise data lakes on Azure. Designed from the start to service multiple petabytes of information while sustaining hundreds of gigabits of throughput, Data Lake Storage Gen2 allows you to easily manage massive amounts of data.

Data processing for AI

Microsoft Fabric Data Factory

With Data Factory, you can ingest, prepare, and transform data from multiple data sources (for example, databases, data warehouse, Lakehouse, real-time data, and more). When you design workloads, this is a tool that can be instrumental in meeting your DataOps requirements.

Data Factory supports both code and no/low code solutions:

  • Data pipelines let you create workflow capabilities at cloud-scale. With data pipelines, you can use the drag-and-drop interface to build workflows that can refresh your dataflow, move petabyte-size data, and define control flow pipelines.

  • Dataflows provide a low-code interface for ingesting data from hundreds of data sources, transforming your data using 300+ data transformations.

Also see:

Azure Databricks

With Databricks Data Intelligence Platform, you can write code to create a machine learning workflow using feature engineering:

  • Data pipelines ingest raw data, create feature tables, train models, and perform batch inference. When you train and log a model using feature engineering in Unity Catalog, the model is packaged with feature metadata. When you use the model for batch scoring or online inference, it automatically retrieves feature values. The caller doesn't need to know about them or include logic to look up or join features to score new data.
  • Model and feature serving endpoints are available with a single click and provide milliseconds of latency.
  • Data and model monitoring.

You can also use Mosaic AI Vector Search, which is optimized for storing and retrieving embeddings. Embeddings are crucial for applications that require similarity searches, such as RAG (Retrieval Augmented Generation), recommendation systems, and image recognition.

Data connectors for AI

Azure Data Factory and Azure Synapse Analytics pipelines support many data stores and formats via Copy, Data Flow, Look up, Get Metadata, and Delete activities. To see the available data store connectors, the supported capabilities and the corresponding configurations, and generic ODBC connection options, see Azure Data Factory and Azure Synapse Analytics connector overview.

Custom AI

Azure Machine Learning

Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project lifecycle. ML professionals, data scientists, and engineers can use it in their day-to-day workflows to train and deploy models and manage machine learning operations (MLOps).

Azure Machine Learning offer the following capabilities:

  • Algorithm selection Some algorithms make particular assumptions about the structure of the data or the desired results. If you can find one that fits your needs, it can give you more useful results, more accurate predictions, or faster training times.

    How to select algorithms for Azure Machine Learning

  • Hyperparameter tuning or optimization is the process of finding the configuration of hyperparameters that results in the best performance. The process is computationally expensive and manual. Hyperparameters are adjustable parameters that let you control the model training process. For example, with neural networks, you decide the number of hidden layers and the number of nodes in each layer. Model performance depends heavily on hyperparameters.

    Azure Machine Learning lets you automate hyperparameter tuning and run experiments in parallel to efficiently optimize hyperparameters.

  • Model training. With Azure Machine Learning, you can iteratively use an algorithm to create or "teach" models. Once trained, these models can then be used to analyze data from which predictions can be made. During the training phase, a quality set of known data is tagged so that individual fields are identifiable. The tagged data is fed to an algorithm configured to make a particular prediction. When finished, the algorithm outputs a model that describes the patterns it found as a set of parameters. During validation, fresh data is tagged and used to test the model. The algorithm is adjusted as needed and possibly put through more training. Finally, the testing phase uses real-world data without any tags or preselected targets. Assuming the model's results are accurate, It's considered ready for use and can be deployed.

  • Automated machine learning (AutoML) is the process of automating the time-consuming, iterative tasks of machine learning model development. It can significantly reduce the time it takes to get production-ready ML models. Automated ML can assist with model selection, hyperparameter tuning, model training, and other tasks, without requiring extensive programming or domain knowledge.

    You can use automated ML when you want Azure Machine Learning to train and tune a model for you using a specified target metric. Automated ML can be used regardless of data science expertise to identify an end-to-end machine learning pipeline for any problem.

    ML professionals and developers across industries can use automated ML to:

    • Implement ML solutions without extensive programming or machine learning knowledge

    • Save time and resources

    • Apply data science best practices

    • Provide agile problem-solving

    • What is automated machine learning?

  • Scoring is also called prediction and is the process of generating values based on a trained machine learning model, given some new input data. The values, or scores, that are created can represent predictions of future values, but they might also represent a likely category or outcome.

  • Feature engineering and featurization. Training data consists of rows and columns. Each row is an observation or record, and the columns of each row are the features that describe each record. Typically, the features that best characterize the patterns in the data are selected to create predictive models.

Although many of the raw data fields can be used directly to train a model, it's often necessary to create other (engineered) features that provide information that better differentiates patterns in the data. This process is called feature engineering, where the use of domain knowledge of the data is used to create features that, in turn, help machine learning algorithms to learn better.

In Azure Machine Learning, data-scaling and normalization techniques are applied to make feature engineering easier. Collectively, these techniques and this feature engineering are called featurization in automated machine learning (ML) experiments.

Azure OpenAI

Azure OpenAI Service lets you tailor OpenAI models to your personal datasets by using a process known as fine-tuning. This customization step lets you get more out of the service by providing:

  • Higher quality results than what you can get just from prompt engineering
  • The ability to train on more examples than can fit into a model's max request context limit.
  • Token savings due to shorter prompts
  • Lower-latency requests, particularly when using smaller models.

For more information, see:

Azure AI services for custom AI

Azure AI services offers features that let you build custom AI models and applications. This section provides an overview some of these key features.

Custom Speech

Custom speech is a feature of the Azure AI Speech service. With custom speech, you can evaluate and improve the accuracy of speech recognition for your applications and products. A custom speech model can be used for real-time speech to text, speech translation, and batch transcription.

Out of the box, speech recognition utilizes a Universal Language Model as a base model that is trained with Microsoft-owned data and reflects commonly used spoken language. The base model is pretrained with dialects and phonetics representing various common domains. When you make a speech recognition request, the most recent base model for each supported language is used by default. The base model works well in most speech recognition scenarios.

A custom model can be used to augment the base model to improve recognition of domain-specific vocabulary specific to the application by providing text data to train the model. It can also be used to improve recognition based for the specific audio conditions of the application by providing audio data with reference transcriptions.

You can also train a model with structured text when the data follows a pattern, to specify custom pronunciations, and to customize display text formatting with custom inverse text normalization, custom rewrite, and custom profanity filtering.

Custom Translator

Custom Translator is a feature of the Azure AI Translator service. With Custom Translator, enterprises, app developers, and language service providers can build customized neural machine translation (NMT) systems. The customized translation systems seamlessly integrate into existing applications, workflows, and websites.

The platform enables users to build and publish custom translation systems to and from English. Custom Translator supports more than three dozen languages that map directly to the languages available for NMT. For a complete list, see Translator language support.

Custom Translator offers the following features:

Feature Description
Apply neural machine translation technology Improve your translation by applying neural machine translation (NMT) provided by Custom translator.
Build systems that knows your business terminology Customize and build translation systems using parallel documents that understand the terminologies used in your own business and industry.
Use a dictionary to build your models If you don't have training data set, you can train a model with only dictionary data.
Collaborate with others Collaborate with your team by sharing your work with different people.
Access your custom translation model You can access your custom translation model anytime using your existing applications/ programs via Microsoft Translator Text API V3.

Document Intelligence custom models

Azure AI Document Intelligence uses advanced machine learning technology to identify documents, detect and extract information from forms and documents, and return the extracted data in a structured JSON output. With Document Intelligence, you can use document analysis models, prebuilt/pretrained, or your trained standalone custom models.

Document Intelligence custom models now include custom classification models for scenarios where you need to identify the document type before invoking the extraction model. A classification model can be paired with a custom extraction model to analyze and extract fields from forms and documents specific to your business. Standalone custom extraction models can be combined to create composed models.

Custom AI tools

Although prebuilt AI models are useful and increasingly flexible, the best way to get what you need from AI is to build a model that's tailored to your specific needs. There are two primary tools for creating custom AI models: Generative AI and traditional machine learning:

Azure Machine Learning studio

Azure Machine Learning studio is a cloud service for accelerating and managing the machine learning (ML) project lifecycle. ML professionals, data scientists, and engineers can use it in their day-to-day workflows to train and deploy models and manage machine learning operations (MLOps).:

  • Build and train Azure Machine Learning model with any type of compute including Spark and GPUs for cloud-scale large AI workloads.
  • Run automated Azure Machine Learning (AutoML) and drag-and-drop UI for low-code Azure Machine Learning.
  • Implement end-to-end Azure Machine LearningOps and repeatable Azure Machine Learning pipelines.
  • Use responsible AI dashboard for bias detection and error analysis.
  • Orchestrate and manage prompt engineering and LLM flows.
  • Deploy models with REST API endpoints, real-time, and batch inference.
  • Use Hubs (Preview) to share compute, quota, security, and connectivity to company resources with a group of workspaces, while centralizing governance for IT. Set up a hub once, then create secure workspaces directly from the Studio for each project. Use hubs to manage your team's work in both ML Studio and AI Studio.

Azure AI Studio

Azure AI Studio is designed to help you efficiently build and deploy custom generative AI applications with the power of the Azure broad AI offerings:

  • Build together as one team. Your AI Studio hub provides enterprise-grade security, and a collaborative environment with shared resources and connections to pretrained models, data and compute.
  • Organize your work. Your AI Studio project helps you save state, allowing you to iterate from first idea, to first prototype, and then first production deployment. Also easily invite others to collaborate along this journey.
  • Use your preferred development platform and frameworks, including GitHub, Visual Studio Code, LangChain, Semantic Kernel, AutoGen, and more.
  • Discover and benchmark from over 1,600 models.
  • Provision Models-as-a-Service (MaaS) through serverless APIs and hosted fine-tuning.
  • Incorporate multiple models, data sources, and modalities.
  • Build Retrieval Augmented Generation (RAG) using your protected enterprise data without the need for fine-tuning.
  • Orchestrate and manage prompts engineering and Large Language Model (LLM) flows.
  • Design and safeguard apps and APIs with configurable filters and controls.
  • Evaluate model responses with built-in and custom evaluation flows.
  • Deploy AI innovations to the Azure managed infrastructure with continuous monitoring and governance across environments.
  • Continuously monitor deployed apps for safety, quality, and token consumption in production.|

For a detailed comparison between Azure Machine Learning and Azure AI Studio, see Azure Machine Learning vs. Azure AI Studio.

Prompt flow in Azure AI Studio

Prompt flow in Azure AI Studio is a development tool designed to streamline the entire development cycle of AI applications powered by Large Language Models (LLMs). Prompt flow provides a comprehensive solution that simplifies the process of prototyping, experimenting, iterating, and deploying your AI applications.

  • Prompt flow is a feature that can be used to generate, customize, or run a flow.
  • A flow is an executable instruction set that can implement the AI logic. Flows can be created or run via multiple tools, like a prebuilt canvas, LangChain, etcetera. Iterations of a flow can be saved as assets; once deployed a flow becomes an API. Not all flows are prompt flows; rather, prompt flow is one way to create a flow.
  • A prompt is a package of input sent to a model, consisting of the user input, system message, and any examples. User input is text submitted in the chat window. System message is a set of instructions to the model scoping its behaviors and functionality.
  • A sample flow is a simple, prebuilt orchestration flow that shows how flows work, and can be customized.
  • A sample prompt is a defined prompt for a specific scenario that can be copied from a library and used as-is or modified in prompt design.

Custom AI code languages

The core concept of AI is the use of algorithms to analyze data and generate models to describe (or score) it in ways that are useful. Algorithms are written by developers and data scientists (and sometimes by other algorithms) using programming code. Two of the most popular programming languages for AI development are currently Python and R.

Python is a general-purpose, high-level programming language. It has a simple, easy-to-learn syntax that emphasizes readability. There is no compiling step. Python has a large standard library, but it also supports the ability to add modules and packages. This encourages modularity and lets you expand capabilities when needed. There is a large and growing ecosystem of AI and ML libraries for Python, including many that are readily available in Azure.

R is a language and environment for statistical computing and graphics. It can be used for everything from mapping broad social and marketing trends online to developing financial and climate models.

Microsoft has fully embraced the R programming language and provides many different options for R developers to run their code in Azure.

General info on custom AI on Azure

Customer stories

Different industries are applying AI in innovative and inspiring ways. Following are a few customer case studies and success stories:

Browse more AI customer stories

General info on Microsoft AI

Learn more about Microsoft AI, and keep up-to-date with related news:

Next steps