Use Azure AI Content Understanding to turn unstructured data into multimodal app experiences.
To better reflect input and material that reflects our real world, artificial intelligence (AI) capabilities are rapidly developing and going beyond traditional text. To make creating multimodal applications containing text, music, photos, and video quicker, simpler, and more affordable, Microsoft Azure launching Azure AI Content Understanding. This service, which is currently in preview, extracts information into adaptable structured outputs using generative AI.
A simplified workflow and the ability to personalize results for a variety of use cases, like call center analytics, marketing automation, content search, and more, are provided via pre-built templates. Additionally, by simultaneously processing data from many modalities, this service can assist developers in simplifying the process of creating AI applications while maintaining accuracy and security.
Develop Multimodal AI Solutions More Quickly with Azure AI Content Understanding.
Overview
Quicken the creation of multimodal AI apps
Businesses may convert unstructured multimodal data into insights with the aid of Azure AI Content Understanding.
Obtain valuable insights from a variety of input data formats, including text, audio, photos, and video.
Use advanced artificial intelligence techniques like scheme extraction and grounding to produce accurate, high-quality data for use in downstream applications.
Simplify and combine pipelines with different kinds of data into a single, efficient process to cut expenses and speed up time to value.
Learn how call center operators and businesses may use call records to extract insightful information that can be used to improve customer service, track key performance indicators, and provide faster, more accurate answers to consumer questions.
Features
Using multimodal AI to turn data into insights
Ingestion of data in multiple modes
Consume a variety of modalities, including documents, photos, voice, and video, and then leverage Azure AI’s array of AI models to convert the incoming data into structured output that downstream applications can readily handle and analyze.
Tailored output schemas
To suit your needs, modify the collected results’ schemas. Make sure that summaries, insights, or features are formatted and structured to only include the most pertinent information from video or audio files, such as timestamps or important points.
Confidence ratings
With user feedback, confidence scores can be used to increase accuracy and decrease the need for human intervention.
Ready-made output for use in subsequent processes
The output can be used by downstream applications to automate business processes using agentic workflows or to develop enterprise generative AI apps using retrieval-augmentation generation (RAG).
Getting grounded
A representation of the extracted, inferred, or abstracted information should be included in the underlying content.
Automated labeling
By employing large language models (LLMs) to extract fields from different document types, you may develop models more quickly and save time and effort on human annotation.
FAQs
What is Azure AI Content Understanding?
In the era of generative AI, Content Understanding is a new Azure AI service that helps businesses speed up the development of multimodal AI apps. Using a variety of input data formats, including text, audio, photos, documents, and video, Content Understanding helps businesses create generative AI solutions with ease using the newest models on the market. AI already recognizes faces, builds bots, and analyzes documents. Without ever needing specialized generative AI skills like prompt engineering, Content Understanding gives businesses a new way to create applications that can integrate all of these using pre-built templates made to address the most common use-cases or by creating custom models to address use-cases that are specific to a given domain or enterprise. With the help of the service, businesses may contribute their domain knowledge and create automated processes that consistently improve output while guaranteeing strong accuracy. Using Azure’s industry-leading enterprise security, data privacy, and ethical AI rules, this new AI service was developed.
What are the benefits of using Azure AI Content Understanding?
Developers can use Content Understanding to develop unique models for their organization and integrate data kinds from multiple modalities into their current apps. For multimodal scenarios, it greatly streamlines the development of generative AI solutions and eliminates the need for manual switching to the most recent model when it is released. It speeds up time-to-value by analyzing several modalities at once in a single workflow.
Where can I learn to use Azure AI Content Understanding?
Check out the Azure AI Studio‘s Azure AI Content Understanding feature.
