Azure OpenAI provides access to many different models, grouped by family and capability. A model family typically associates models by their intended task. The following table describes model families currently available in Azure OpenAI. Not all models are available in all regions currently. Refer to the model capability table in this article for a full breakdown.
| Model family | Description |
|---|---|
| GPT-4 | A set of models that improve on GPT-3.5 and can understand as well as generate natural language and code. |
| GPT-3 | A series of models that can understand and generate natural language. This includes the new ChatGPT model. |
| DALL-E (Preview) | A series of models in preview that can generate original images from natural language. |
| Codex | A series of models that can understand and generate code, including translating natural language to code. |
| Embeddings | A set of models that can understand and use embeddings. An embedding is a special format of data representation that can be easily utilized by machine learning models and algorithms. The embedding is an information dense representation of the semantic meaning of a piece of text. Currently, we offer three families of Embeddings models for different functionalities: similarity, text search, and code search. |
- Azure Cognitive Services: A suite of AI-powered APIs that enable developers to add capabilities like speech recognition, natural language understanding, computer vision, sentiment analysis, and more to their applications.
- Azure Machine Learning: A cloud-based service that provides a complete environment for building, training, and deploying machine learning models at scale. It offers various tools and frameworks for data preparation, model training, and model deployment.
- Azure Bot Services: A platform for building, deploying, and managing intelligent chatbots that can interact with users through multiple channels, including websites, messaging platforms, and voice assistants.
- Azure Databricks: A fast, easy, and collaborative Apache Spark-based analytics platform. It provides an environment for processing and analyzing large datasets, performing machine learning tasks, and building data pipelines.
- Azure Cognitive Search: A fully managed cloud search service that allows developers to integrate advanced search capabilities into their applications. It enables features like full-text search, faceted navigation, and geo-search across structured and unstructured data.
- Azure Speech Service: A cloud-based service that converts spoken language into written text and vice versa. It offers capabilities like speech-to-text transcription, text-to-speech synthesis, and real-time speech translation.
- Azure Video Analyzer: A platform that leverages AI to extract insights and intelligence from video streams. It enables applications such as object detection, facial recognition, people counting, and activity detection in real-time video.
- Azure Form Recognizer: A service that automates the extraction of structured data from documents. It can analyze invoices, receipts, forms, and other documents, extracting key information and making it available for further processing.
- Azure Personalizer: A reinforcement learning-based service that helps developers deliver personalized experiences to their users. It learns from user feedback and behavior to optimize and recommend the most relevant content or actions.
- Azure Metrics Advisor: A service that uses AI to automatically detect and diagnose anomalies in time-series data. It can monitor metrics from various sources and provide alerts and insights when unusual patterns or behaviors are detected.
I’m Rajesh Kumar, a DevOps, SRE, DevSecOps, Cloud, and Platform Engineering expert passionate about sharing practical knowledge, real-world experiences, and industry best practices. I have worked at Cotocus and regularly write about technology, travel, investing, health, product reviews, and digital marketing through my various platforms.
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