Azure AI/ML Service

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An Azure AI/ML Service is a cloud-based AI/ML service that is an Azure service.

  • AKA: Microsoft Azure AI Engine, Azure AI Platform.
  • Context:
    • It can include Azure Cognitive Services, which provide a range of AI functionalities such as vision, speech, language, and decision-making capabilities.
    • It can be associated with Azure Machine Learning, a service for building, training, and deploying machine learning models at scale.
    • It can offer integration with popular ML frameworks like TensorFlow, PyTorch, and Scikit-learn.
    • It can support Automated Machine Learning, which simplifies model development by automating algorithm selection and hyperparameter tuning.
    • It can leverage Azure AI Infrastructure for scalable and secure AI workloads, trusted by major AI leaders like OpenAI and Nvidia.
    • It can facilitate the development of AI applications using tools like Azure AI Studio and Visual Studio Code.
    • It can promote responsible AI practices with built-in tools for governance, security, and compliance.
    • It can provide pre-built AI capabilities
    • It can provide custom AI development through APIs and SDKs.
    • It can integrate with other Azure services such as Azure Synapse Analytics for comprehensive data and AI solutions.
    • ...
  • Example(s):
    • Azure Cognitive Services, 2016: Microsoft launches Azure Cognitive Services, providing pre-built AI models and APIs for developers to incorporate into their applications.
    • Azure Machine Learning, 2018: Azure Machine Learning service becomes generally available, offering a cloud-based environment for training, deploying, and managing machine learning models.
    • Azure AI, 2019: Microsoft introduces Azure AI, a comprehensive set of AI services for developers and data scientists.
    • Azure Applied AI Services, 2020: Azure Applied AI Services are introduced, providing task-specific AI models for common business scenarios.
    • Azure OpenAI Service Preview, 2021: Azure OpenAI Service is announced in preview, allowing developers to access OpenAI's powerful language models through Azure.
    • Azure OpenAI Service GA, 2022: Azure OpenAI Service becomes generally available, providing broader access to GPT-3 and other advanced language models.
    • Azure AI Studio, 2023: Microsoft launches Azure AI Studio, a unified platform for AI development that includes tools for model selection, fine-tuning, and responsible AI practices.
    • Azure AI Search, 2023: Azure AI Search (formerly Azure Cognitive Search) is introduced, offering advanced vector search capabilities for AI-powered information retrieval.
    • ...
  • Counter-Example(s):
  • See: GCP Vertex AI, AWS SageMaker, Azure OpenAI On Your Data Feature.

Referneces

2023

  • chat
    • Azure AI is a set of artificial intelligence (AI) and machine learning (ML) services provided by Microsoft Azure, which is a cloud computing platform. Azure AI aims to help developers and data scientists build, deploy, and manage AI solutions using a variety of tools, frameworks, and services. These services include pre-built AI capabilities, machine learning tools, and custom AI development platforms.
    • Azure Machine Learning is a specific service within Azure AI that focuses on providing tools, services, and platforms to build, train, and deploy machine learning models. It offers a cloud-based environment that enables developers and data scientists to collaboratively work on model development, testing, and deployment. Some key features of Azure Machine Learning include:
      • Drag-and-drop designer: A visual interface for building, training, and deploying machine learning models without the need for coding.
      • Automated machine learning: A feature that automates the process of selecting the best algorithm, data preprocessing, and hyperparameter tuning for a given problem, thus reducing the time and effort required for model development.
      • MLOps (Machine Learning Operations): Tools and services to manage the end-to-end machine learning lifecycle, including model versioning, monitoring, and deployment.
      • Integration with popular open-source frameworks and tools: Azure Machine Learning supports popular ML frameworks like TensorFlow, PyTorch, and Scikit-learn, and integrates with tools like Jupyter notebooks for a seamless development experience.
      • Scalable compute resources: Azure Machine Learning allows you to leverage the power of the cloud to train and deploy models on demand, using GPU and CPU clusters.

2023

  • (MS Azure, 2023) ⇒ https://azure.microsoft.com/en-us/solutions/ai/#overview
    • Azure AI — a portfolio of artificial intelligence (AI) services designed for developers and data scientists — to do more with less. Take advantage of the decades of breakthrough research, responsible AI practices, and flexibility that Azure AI offers to build and deploy your own AI solutions. Access high-quality vision, speech, language, and decision-making AI models through simple API calls, and create your own machine learning models using an AI supercomputing infrastructure, familiar tools like Jupyter Notebooks and Visual Studio Code, and open-source frameworks like TensorFlow and PyTorch—all backed by Microsoft's responsible AI principles.
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