Azure Machine Learning (ML) Platform Service
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Azure Machine Learning (ML) Platform Service is an ML PaaS that is a Microsoft Azure Service.
- Context:
- It is within Azure AI.
- It includes tools and services for preparing data, training models, deploying models, and managing models.
- It provides a variety of machine learning algorithms including supervised learning, unsupervised learning, and reinforcement learning.
- It provides deployment options such as: batch scoring, real-time scoring, and model serving.
- It can speed up the process of building and deploying machine learning models, help build accurate models, scale to meet demanding workloads, and is cost-effective.
- It can be accessed via Azure CLI ml extension (e.g. v2), and Python SDK azure-ai-ml (e.g. v2).
- It can be utilized by a Azure ML-using System.
- It can be associated with Azure ML Service Pricing.
- …
- Example(s):
- 2014: Azure ML is launched.
- 2017: Azure ML Studio is released.
- 2018: Azure ML Pipelines is released.
- 2019: Azure ML Model Management is released.
- 2020: Azure ML is integrated with Azure Synapse Analytics.
- 2021: Azure ML is integrated with Azure Databricks.
- 2022: Azure ML is integrated with Azure Kubernetes Service.
- …
- Counter-Example(s):
- See: Azure Data Factory, Azure Pipelines, Machine Learning Platform, Azure OpenAI Services.
References
2023
- https://learn.microsoft.com/en-us/AZURE/machine-learning/concept-azure-machine-learning-v2?view=azureml-api-2&viewFallbackFrom=azureml-api-1&tabs=cli
- QUOTE: Azure Machine Learning includes several resources and assets to enable you to perform your machine learning tasks. These resources and assets are needed to run any job.
- Resources: setup or infrastructural resources needed to run a machine learning workflow. Resources include:
- Assets: created using Azure Machine Learning commands or as part of a training/scoring run. Assets are versioned and can be registered in the Azure Machine Learning workspace. They include:
- QUOTE: Azure Machine Learning includes several resources and assets to enable you to perform your machine learning tasks. These resources and assets are needed to run any job.
2022
- https://learn.microsoft.com/en-us/azure/architecture/solution-ideas/articles/azure-machine-learning-solution-architecture
- QUOTE: This architecture shows you the components used to build, deploy, and manage high-quality models with Azure Machine Learning, a service for the end-to-end ML lifecycle.
- QUOTE: This architecture shows you the components used to build, deploy, and manage high-quality models with Azure Machine Learning, a service for the end-to-end ML lifecycle.
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Microsoft_Azure#Machine_learning Retrieved:2019-1-6.
- Machine Learning:
- Microsoft Azure Machine Learning (Azure ML) service is part of Cortana Intelligence Suite that enables predictive analytics and interaction with data using natural language and speech through Cortana.
- Cognitive Services (formerly Project Oxford) are a set of APIs, SDKs and services available to developers to make their applications more intelligent, engaging and discoverable
- Machine Learning:
2014
- (Microsoft Azure, 2014) ⇒ http://azure.microsoft.com/en-us/documentation/articles/machine-learning-faq/
- QUOTE: Microsoft Azure Machine Learning is fully managed service to create, test, operationalize and manage predictive analytics solutions in the cloud. With just a browser, you can now sign up to Azure Machine Learning, upload data and immediately start machine learning experiments. Visual composition, large pallet of modules and a library of starting templates makes common machine learning tasks simple and quick. Turning a model into web service is easy - with a few clicks, a Predictive model build in ML Studio can be turned into a public REST API that encapsulates custom data transformation logic and sophisticated machine learning models.