Breyta

Deila með

Artificial intelligence (AI) architecture design

  • Grein

Artificial intelligence (AI) is the capability of a computer to imitate intelligent human behavior. Through AI, machines can analyze images, comprehend speech, interact in natural ways, and make predictions using data.

Illustration depicting the relationship of artificial intelligence as a parent concept. Within AI is machine learning. Within machine learning is deep learning.

AI concepts

Algorithm

An algorithm is a sequence of calculations and rules used to solve a problem or analyze a set of data. It is like a flow chart, with step-by-step instructions for questions to ask, but written in math and programming code. 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.

Machine learning

Machine learning is an AI technique that uses mathematical algorithms to create predictive models. An 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.

Deep learning

Deep learning is a type of ML that can determine for itself whether its predictions are accurate. It also uses algorithms to analyze data, but it does so on a larger scale than ML.

Deep learning uses artificial neural networks, which consist of multiple layers of algorithms. Each layer looks at the incoming data, performs its own specialized analysis, and produces an output that other layers can understand. This output is then passed to the next layer, where a different algorithm does its own analysis, and so on.

With many layers in each neural network-and sometimes using multiple neural networks-a machine can learn through its own data processing. This requires much more data and much more computing power than ML.

Bots

A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body. Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web content.

Bots have become much more sophisticated, using AI and other technologies to mimic human activity and decision-making, often while interacting directly with humans through text or even speech. Examples include bots that can take a dinner reservation, chatbots (or conversational AI) that help with customer service interactions, and social bots that post breaking news or scientific data to social media sites.

Microsoft offers the Azure Bot Service, a managed service purpose-built for enterprise-grade bot development.

Autonomous systems

Autonomous systems are part of an evolving new class that goes beyond basic automation. Instead of performing a specific task repeatedly with little or no variation (like bots do), autonomous systems bring intelligence to machines so they can adapt to changing environments to accomplish a desired goal.

Smart buildings use autonomous systems to automatically control operations like lighting, ventilation, air conditioning, and security. A more sophisticated example would be a self-directed robot exploring a collapsed mine shaft to thoroughly map its interior, determine which portions are structurally sound, analyze the air for breathability, and detect signs of trapped miners in need of rescue-all without a human monitoring in real time on the remote end.

General info on Microsoft AI

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

High-level architectural types

Prebuilt AI

Prebuilt AI is exactly what it sounds like-off-the-shelf AI models, services, and APIs that are ready to use. These help you add intelligence to apps, websites, and flows without having to gather data and then build, train, and publish your own models.

One example of prebuilt AI might be a pretrained model that can be incorporated as is or used to provide a baseline for further custom training. Another example would be a cloud-based API service that can be called at will to process natural language in a desired fashion.

Azure AI services

Cognitive Services provide developers the opportunity to use prebuilt APIs and integration toolkits to create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure AI services can be categorized into five main pillars: Vision, Speech, Language, Web Search, and Decision/Recommendation.

Prebuilt AI models in AI Builder

AI Builder is a new capability in Microsoft Power Platform that provides a point-and-click interface for adding AI to your apps, even if you have no coding or data science skills. (Some features in AI Builder have not yet released for general availability and remain in preview status. For more information, refer to the Feature availability by region page.)

You can build and train your own models, but AI Builder also provides select prebuilt AI models that are ready for use right away. For example, you can add a component in Microsoft Power Apps based on a prebuilt model that recognizes contact information from business cards.

Custom AI

Although prebuilt AI is useful (and increasingly flexible), the best way to get what you need from AI is probably to build a system yourself. This is obviously a very deep and complex subject, but let's look at some basic concepts beyond what we've just covered.

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.

Training

Training is core to machine learning. It is the iterative process of "teaching" an algorithm to create models, which are used to analyze data and then make accurate predictions from it. In practice, this process has three general phases: training, validation, and testing.

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 is considered ready for use and can be deployed.

Hyperparameter tuning

Hyperparameters are data variables that govern the training process itself. They are configuration variables that control how the algorithm operates. Hyperparameters are thus typically set before model training begins and are not modified within the training process in the way that parameters are. Hyperparameter tuning involves running trials within the training task, assessing how well they are getting the job done, and then adjusting as needed. This process generates multiple models, each trained using different families of hyperparameters.

Model selection

The process of training and hyperparameter tuning produces numerous candidate models. These can have many different variances, including the effort needed to prepare the data, the flexibility of the model, the amount of processing time, and of course the degree of accuracy of its results. Choosing the best trained model for your needs and constraints is called model selection, but this is as much about preplanning before training as it is about choosing the one that works best.

Automated machine learning (AutoML)

Automated machine learning, also known as 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.

Scoring

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. The scoring process can generate many different types of values:

  • A list of recommended items and a similarity score

  • Numeric values, for time series models and regression models

  • A probability value, indicating the likelihood that a new input belongs to some existing category

  • The name of a category or cluster to which a new item is most similar

  • A predicted class or outcome, for classification models

Batch scoring is when data is collected during some fixed period of time and then processed in a batch. This might include generating business reports or analyzing customer loyalty.

Real-time scoring is exactly that-scoring that is ongoing and performed as quickly as possible. The classic example is credit card fraud detection, but real-time scoring can also be used in speech recognition, medical diagnoses, market analyses, and many other applications.

General info on custom AI on Azure

Azure AI platform offerings

Following is a breakdown of Azure technologies, platforms, and services you can use to develop AI solutions for your needs.

Azure Machine Learning

This is an enterprise-grade machine learning service to build and deploy models faster. Azure Machine Learning offers web interfaces and SDKs so you can quickly 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

Azure automated machine learning

Azure provides extensive support for automated ML. Developers can build models using a no-code UI or through a code-first notebooks experience.

Azure AI services

This is a comprehensive family of AI services and cognitive APIs to help you build intelligent apps. These domain-specific, pretrained AI models can be customized with your data.

This is an AI-powered cloud search service for mobile and web app development. The service can search over private heterogeneous content, with options for AI enrichment if your content is unstructured or unsearchable in raw form.

Azure Bot Service

This is a purpose-built bot development environment with out-of-the-box templates to get started quickly.

Apache Spark on Azure

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 much faster than disk-based applications, such as Hadoop.

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.

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 a ready-to-go 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.

Customer stories

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

Browse more AI customer stories

Next steps