Azure OpenAI, A New Microsoft Service, Allows GPT-3 Access

OpenAI's machine learning models are now available on Microsoft's Azure platform thanks to a new service called the Azure OpenAI Service.

Microsoft announced the preview of the Azure OpenAI Service, which enables access to OpenAI’s API via the Azure platform. Customers will gain access to OpenAI’s strong GPT-3 models through this new Azure Cognitive Service. In addition, access is provided with enterprise-grade security, reliability, compliance, data privacy, and other features.

Large Language Model

Azure OpenAI service can customise the GPT-3 model to handle applications that require a strong command of the language. For example, converting natural language to software code, summarising enormous volumes of information, and creating responses to inquiries are among the most important tasks. In addition, Microsoft’s inclusion of the OpenAI API in Azure will allow businesses to deploy GPT-3 by local laws, regulations, and technical restrictions.

Microsoft continues to diversify its AI language services portfolio with Azure OpenAI. Additionally, it keeps up with Google and AWS, its main public cloud competitors. For example, when it comes to speech recognition, Google was one of the first companies to use neural networks. Speech recognition is accomplished via the general availability of Cloud Text-to-Speech and Cloud Speech-to-Text in 2018. And, from the same year, AWS has offered Amazon Transcribe.


Since the launch of OpenAI, users have discovered an infinite number of applications for these AI models‘ deep and extensive comprehension of language.

“What makes GPT-3 so exciting is that we are only just beginning to comprehend its potency and potential,” said Eric Boyd, corporate vice president for Azure AI at Microsoft. “Now, we’re taking what OpenAI has provided and packaging it with all the enterprise guarantees necessary for organisations to get into production.”

GPT-3 is a new class of models tailored to meet a wide variety of application requirements. The GPT-3 use cases necessitate a thorough comprehension of language, beginning with converting natural language to software code.

OpenAI and Microsoft

According to OpenAI CEO Sam Altman, the models become more capable as more people use and access them when you can communicate with a machine in clear language, even when the request is ambiguous, such as “what do you want?”; he looks forward to that day.

“GPT-3 has truly established itself as the first robust, general-purpose model for natural language — GPT-3 is a single model that can be used for all of these things, which developers appreciate because it enables rapid experimentation,” Altman said. “For a long time, we’ve desired to scale it as widely as possible, which is one of the reasons we’re so excited about our cooperation with Microsoft.”

Additionally, users may quickly train the models that have already mastered language nuances – particularly through the process of absorbing patterns from billions of pages of publically available literature. Users need to show the models a few samples of the outputs, answers, or code they want them to generate in a process called “few-shot learning.”

“It truly is a new paradigm in which this enormous model has become the platform,” Microsoft’s Boyd explained. “Businesses are interested in GPT-3 because it is both powerful and easy to use.”

GPT-3 may be used in the enterprise for various purposes, from summarising common customer service complaints to assisting developers in writing speedier code. These features are accomplished without pausing to look for examples or creating fresh content for blog posts, according to Dominic Divakaruni, Microsoft group product manager for Azure OpenAI.


The Azure OpenAI Service is the latest solution to result from Microsoft’s cooperation with OpenAI. The goal is to accelerate AI breakthroughs by constructing the first supercomputer on Azure in collaboration with Microsoft and commercialising new AI technology.

For further information, read here.

More Great AIM Stories

Dr. Nivash Jeevanandam
Nivash holds a doctorate in information technology and has been a research associate at a university and a development engineer in the IT industry. Data science and machine learning excite him.

More Stories


8th April | In-person Conference | Hotel Radisson Blue, Bangalore

Organized by Analytics India Magazine

View Event >>

30th Apr | Virtual conference

Organized by Analytics India Magazine

View Event >>

Yugesh Verma
All you need to know about Graph Embeddings

Embeddings can be the subgroups of a group, similarly, in graph theory embedding of a graph can be considered as a representation of a graph on a surface, where points of that surface are made up of vertices and arcs are made up of edges

Yugesh Verma
A beginner’s guide to Spatio-Temporal graph neural networks

Spatio-temporal graphs are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features of the graph. Neural networks which are developed to deal with time-varying features of the graph can be considered as Spatio-temporal graph neural networks. 

Vijaysinh Lendave
How to Evaluate Recommender Systems with RGRecSys?

A recommender system, sometimes known as a recommendation engine, is a type of information filtering system that attempts to forecast a user’s “rating” or “preference” for an item. In this post, we will look at RGRecSys, a library that performs constraint evaluation of recommender systems.

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM