Temporary Disabled. :) please Go back Azure Machine Learning - ML as a Service | Microsoft Azure www.fgks.org » Address: [go: up one dir, main page] Include Form Remove Scripts Accept Cookies Show Images Show Referer Rotate13 Base64 Strip Meta Strip Title Session Cookies Skip Navigation Contact Sales Search Search My account Portal Sign in Sales: : Find a local number My account Portal Sign in Free account Overview Solutions Products Featured Featured Explore some of the most popular Azure products Virtual MachinesProvision Windows and Linux virtual machines in seconds Windows Virtual DesktopThe best virtual desktop experience, delivered on Azure Azure SQLManaged, always up-to-date SQL instance in the cloud App ServiceQuickly create powerful cloud apps for web and mobile Azure Cosmos DBFast NoSQL database with open APIs for any scale PlayFabThe complete LiveOps back-end platform for building and operating live games Azure Kubernetes Service (AKS)Simplify the deployment, management, and operations of Kubernetes Azure FunctionsProcess events with serverless code Azure Cognitive ServicesAdd smart API capabilities to enable contextual interactions Azure QuantumExperience quantum impact today on Azure See more AI + Machine Learning AI + Machine Learning Create the next generation of applications using artificial intelligence capabilities for any developer and any scenario Azure Cognitive ServicesAdd smart API capabilities to enable contextual interactions Azure Bot ServicesIntelligent, serverless bot services that scale on demand Machine LearningBuild, train, and deploy models from the cloud to the edge Azure DatabricksFast, easy, and collaborative Apache Spark-based analytics platform Azure Cognitive SearchAI-powered cloud search service for mobile and web app development Azure PerceptAccelerate edge intelligence from silicon to service See more Analytics Analytics Gather, store, process, analyze, and visualize data of any variety, volume, or velocity Azure Synapse 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Develop and manage your containerized applications faster with integrated tools Azure Kubernetes Service (AKS)Simplify the deployment, management, and operations of Kubernetes Container InstancesEasily run containers on Azure without managing servers Service FabricDevelop microservices and orchestrate containers on Windows or Linux Container RegistryStore and manage container images across all types of Azure deployments Web App for ContainersEasily deploy and run containerized web apps that scale with your business Azure FunctionsProcess events with serverless code Azure Red Hat OpenShiftFully managed OpenShift service, operated jointly by Microsoft and Red Hat See more Databases Databases Support rapid growth and innovate faster with secure, enterprise-grade, and fully managed database services Azure SQLManaged, always up-to-date SQL instance in the cloud Azure Cosmos DBFast NoSQL database with open APIs for any scale Azure SQL DatabaseManaged, intelligent SQL in the cloud Azure 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and developers with a wide range of productive experiences to build, train, and deploy machine learning models and foster team collaboration. Accelerate time to market with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible machine learning. To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video. Machine Learning for all skill levels Productivity for all skill levels, with Jupyter notebooks, drag-and-drop designer, and automated machine learning. End-to-end MLOps Robust MLOps capabilities that enable creation and deployments of models at scale using automated and reproducible machine learning workflows. Responsible machine learning innovation Rich set of built-in responsible capabilities to understand, protect, and control data, models and processes. Open and interoperable Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. Learn how to build models securely across hybrid and multicloud environments with Azure Machine Learning and Azure Arc. Read the announcement. Boost productivity with machine learning for all skills Rapidly build and deploy machine learning models using tools that meet your needs regardless of skill level. Use built-in Jupyter Notebooks with Intellisense or the drag-and-drop designer. Accelerate model creation with automated machine learning, and access powerful feature engineering, algorithm selection, and hyperparameter-sweeping capabilities. Increase team efficiency with shared datasets, notebooks, models, and customizable dashboards that track all aspects of the machine learning process. Operationalize at scale with MLOps Take advantage of MLOps to streamline the machine learning lifecycle, from building models to deployment and management. Create reproducible workflows with machine learning pipelines, and train, validate and deploy thousands of models at scale, from the cloud to the edge. Use Azure DevOps or GitHub Actions to schedule, manage, and automate the machine learning pipelines, and use advanced data-drift analysis to improve model performance over time. Build responsible machine learning solutions Access state-of-the-art responsible machine learning capabilities to understand, protect, and control your data, models, and processes. Explain model behavior during training and inferencing, and build for fairness by detecting and mitigating model bias. Preserve data privacy throughout the machine learning lifecycle with differential privacy techniques, and use confidential computing to secure machine learning assets. Automatically maintain audit trails, track lineage, and use model datasheets to enable accountability. Innovate on an open and flexible platform Get built-in support for open-source tools and frameworks for machine learning model training and inferencing. Use familiar frameworks like PyTorch, TensorFlow, or scikit-learn, or the open and interoperable ONNX format. Choose the development tools that best meet your needs, including popular IDEs, Jupyter Notebooks, and CLIs, or languages such as Python and R. Use ONNX Runtime to optimize and accelerate inferencing across cloud and edge devices. Track all aspects of your training experiments using MLfLow. Build your machine learning skills with Azure Learn more about machine learning on Azure and participate in hands-on tutorials with this 30-day learning journey. At the end of this learning journey, you'll be prepared to take the Azure Data Scientist Associate Certification. Get helpful resources on machine learning with Azure Advanced security, governance, and hybrid infrastructure Train models on your hybrid infrastructure using Kubernetes clusters on-premises, across multicloud environments, and at the edge with Azure Arc interoperability. Access security capabilities such as role-based access, custom machine learning roles, virtual networks, and private links. Manage governance with policies, audit trails, quota, and cost management. Streamline compliance with a comprehensive portfolio spanning 60 certifications, including FedRAMP High and DISA IL5. Learn more about security on Azure Key service capabilities Collaborative notebooks Maximize productivity with IntelliSense, easy compute and kernel switching, and offline notebook editing. Automated machine learning Rapidly create accurate models for classification, regression, and time-series forecasting. Use model interpretability to understand how the model was built. Drag-and-drop machine learning Use machine learning tools like designer with modules for data transformation, model training, and evaluation, or to easily create and publish machine learning pipelines. Data labeling Prepare data quickly, manage and monitor labeling projects, and automate iterative tasks with machine learning–assisted labeling. MLOps Use the central registry to store and track data, models, and metadata. Automatically capture lineage and governance data. Use Git to track work and GitHub Actions to implement workflows. Manage and monitor runs, or compare multiple runs for training and experimentation. Autoscaling compute Use managed compute to distribute training and to rapidly test, validate, and deploy models. Share CPU and GPU clusters across a workspace and automatically scale to meet your machine learning needs. Deep integration with other Azure services Accelerate productivity with built-in integration with Microsoft Power BI and Azure services such as Azure Synapse Analytics, Azure Cognitive Search, Azure Data Factory, Azure Data Lake, Azure Arc and Azure Databricks. Hybrid and multicloud support Run machine learning on existing Kubernetes clusters on-premises, in multi-cloud and at the edge with Azure Arc. Use the simple one-click deploy ML agent to start training models securely, wherever your data lives. Reinforcement learning Scale reinforcement learning to powerful compute clusters, support multi-agent scenarios, and access open-source reinforcement learning algorithms, frameworks, and environments. Responsible machine learning Get model transparency at training and inferencing with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. Protect data with differential privacy and confidential machine learning pipelines. Enterprise-grade security Build and deploy models more securely with network isolation and private link capabilities, role-based access control for resources and actions, custom roles, and managed identity for compute resources. Cost management Better manage resource allocations for Azure Machine Learning compute instances with workspace- and resource-level quota limits. Pay only for what you need, with no upfront cost See Azure Machine Learning pricing Mastering Azure Machine Learning Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes. Principles of Data Science Many people working with data have developed skills in math, programming, or domain expertise, but proper data science calls for all three. This comprehensive e-book helps fill in the gaps. A Forrester Wave Leader 2020 Forrester names Microsoft Azure Machine Learning a leader in The Forrester Waveâ„¢: Notebook-Based Predictive Analytics And Machine Learning, Q3 2020. How to use Azure Machine Learning Go to your studio web experience Build and train Deploy and manage Go to your studio web experience 1 steps Build and train 1 steps Deploy and manage 1 steps Step 1 of 1 Author new models and store your compute targets, models, deployments, metrics, and run histories in the cloud. Step 1 of 1 Use automated machine learning to identify algorithms and hyperparameters and track experiments in the cloud. Author models using notebooks or the drag-and-drop designer. Step 1 of 1 Deploy your machine learning model to the cloud or the edge, monitor performance, and retrain it as needed. Resources Beginner tutorials Get started with machine learning and the Python SDK Get started with Jupyter Notebooks Get started with automated machine learning Use the designer tool for drag-and-drop machine learning Get started on your 30 day learning journey Advanced tutorials Train and deploy automated machine learning models Explore MLOps examples in GitHub Use the designer tool for prediction Interpret and explain machine learning models Interpret and explain automated machine learning models Featured videos Get started with Azure Machine Learning Drag and drop machine learning with designer Automated machine learning for rapid model building Using Notebooks effectively Manage the MLOps lifecycle Build models responsibly Increase productivity with data labeling Additional resources What is machine learning? Machine learning algorithms Start using Azure Machine Learning today Get instant access and a $200 credit by signing up for an Azure free account. Sign in to the Azure portal. Explore the documentation and tutorials, and browse quickstarts and developer resources. Customers using Azure Machine Learning "The model we deployed on Azure Machine Learning helped us choose the three new retail locations we opened in 2019. Those stores exceeded their revenue plans by over 200 percent in December, the height of our season, and within months of opening were among the best-performing stores in their districts." Jolie Vitale, Director of BI and Analytics, Carhartt Read the story "MLOps is at the core of our product. Because of its reproducible ML pipelines, ... registered models, and automatic model scoring, we're definitely detecting things that we missed before. Which, in terms of risk management, is really, really important." Ignasi Paredes-Oliva, Lead Data Scientist, Nestlé Global Security Operations Center Read the story "Azure Machine Learning allows us to manage the entire lifecycle, from experimentation and development to production and enhancements." Joey Chua, Senior Manager Machine Learning Engineering, AGL Read the story "With model interpretability in Azure Machine Learning, we have a high degree of confidence that our machine learning model is generating meaningful and fair results." Daniel Engberg, Head of Data Analytics and Artificial Intelligence, Scandinavian Airlines Read the story "If I have 200 models to train—I can just do this all at once. It can be farmed out to a huge compute cluster, and it can be done in minutes. So I'm not waiting for days." Dean Riddlesden, Senior Data Scientist, Global Analytics, Walgreens Boots Alliance Read the story "We see Azure Machine Learning and our partnership with Microsoft as critical to driving increased adoption and acceptance of AI from the regulators." Alex Mohelsky, Partner and Advisory Data, Analytic, and AI Leader, EY Canada Read the story Azure Machine Learning updates, blogs, and announcements ANNOUNCEMENT Azure Open Datasets, now in preview, offers access to curated datasets. UPDATE Action required: upgrade your AML cluster to Ubuntu 18.04 LTS by 30 April 2021 UPDATE Public preview: Azure Machine Learning VS Code Integration UPDATE Azure Machine Learning - Public preview announcements Ignite, March 2021. UPDATE Azure Machine Learning - Generally available announcements at Ignite, March 2021. UPDATE Azure Machine Learning - Arc enabled machine learning UPDATE General availability: Azure Machine Learning updates for native terminal UPDATE Public preview: Azure Machine Learning - Responsible ML updates UPDATE General availability: Azure Machine Learning Output Datasets UPDATE Public preview: Azure Machine Learning Data Labeling – Image Instance Segmentation Frequently asked questions about Azure Machine Learning Where is Azure Machine Learning available? The service is generally available in several countries/regions, with more on the way. What is the SLA? The service-level agreement (SLA) for Azure Machine Learning is 99.9 percent uptime. What is Azure Machine Learning studio? Azure Machine Learning studio is the top-level resource for Machine Learning. This capability provides a centralized place for data scientists and developers to work with all the artifacts for building, training, and deploying machine learning models. Ready when you are—let’s set up your Azure free account Start free Chat with Sales
Enterprise-grade machine learning service to build and deploy models faster
Empower data scientists and developers with a wide range of productive experiences to build, train, and deploy machine learning models and foster team collaboration. Accelerate time to market with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible machine learning.
To view this video please enable JavaScript, and consider upgrading to a web browser that supports HTML5 video.
Productivity for all skill levels, with Jupyter notebooks, drag-and-drop designer, and automated machine learning.
Robust MLOps capabilities that enable creation and deployments of models at scale using automated and reproducible machine learning workflows.
Rich set of built-in responsible capabilities to understand, protect, and control data, models and processes.
Best-in-class support for open-source frameworks and languages including MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R.
Rapidly build and deploy machine learning models using tools that meet your needs regardless of skill level. Use built-in Jupyter Notebooks with Intellisense or the drag-and-drop designer. Accelerate model creation with automated machine learning, and access powerful feature engineering, algorithm selection, and hyperparameter-sweeping capabilities. Increase team efficiency with shared datasets, notebooks, models, and customizable dashboards that track all aspects of the machine learning process.
Take advantage of MLOps to streamline the machine learning lifecycle, from building models to deployment and management. Create reproducible workflows with machine learning pipelines, and train, validate and deploy thousands of models at scale, from the cloud to the edge. Use Azure DevOps or GitHub Actions to schedule, manage, and automate the machine learning pipelines, and use advanced data-drift analysis to improve model performance over time.
Access state-of-the-art responsible machine learning capabilities to understand, protect, and control your data, models, and processes. Explain model behavior during training and inferencing, and build for fairness by detecting and mitigating model bias. Preserve data privacy throughout the machine learning lifecycle with differential privacy techniques, and use confidential computing to secure machine learning assets. Automatically maintain audit trails, track lineage, and use model datasheets to enable accountability.
Get built-in support for open-source tools and frameworks for machine learning model training and inferencing. Use familiar frameworks like PyTorch, TensorFlow, or scikit-learn, or the open and interoperable ONNX format. Choose the development tools that best meet your needs, including popular IDEs, Jupyter Notebooks, and CLIs, or languages such as Python and R. Use ONNX Runtime to optimize and accelerate inferencing across cloud and edge devices. Track all aspects of your training experiments using MLfLow.
Learn more about machine learning on Azure and participate in hands-on tutorials with this 30-day learning journey. At the end of this learning journey, you'll be prepared to take the Azure Data Scientist Associate Certification.
Maximize productivity with IntelliSense, easy compute and kernel switching, and offline notebook editing.
Rapidly create accurate models for classification, regression, and time-series forecasting. Use model interpretability to understand how the model was built.
Use machine learning tools like designer with modules for data transformation, model training, and evaluation, or to easily create and publish machine learning pipelines.
Prepare data quickly, manage and monitor labeling projects, and automate iterative tasks with machine learning–assisted labeling.
Use the central registry to store and track data, models, and metadata. Automatically capture lineage and governance data. Use Git to track work and GitHub Actions to implement workflows. Manage and monitor runs, or compare multiple runs for training and experimentation.
Use managed compute to distribute training and to rapidly test, validate, and deploy models. Share CPU and GPU clusters across a workspace and automatically scale to meet your machine learning needs.
Accelerate productivity with built-in integration with Microsoft Power BI and Azure services such as Azure Synapse Analytics, Azure Cognitive Search, Azure Data Factory, Azure Data Lake, Azure Arc and Azure Databricks.
Run machine learning on existing Kubernetes clusters on-premises, in multi-cloud and at the edge with Azure Arc. Use the simple one-click deploy ML agent to start training models securely, wherever your data lives.
Scale reinforcement learning to powerful compute clusters, support multi-agent scenarios, and access open-source reinforcement learning algorithms, frameworks, and environments.
Get model transparency at training and inferencing with interpretability capabilities. Assess model fairness through disparity metrics and mitigate unfairness. Protect data with differential privacy and confidential machine learning pipelines.
Build and deploy models more securely with network isolation and private link capabilities, role-based access control for resources and actions, custom roles, and managed identity for compute resources.
Better manage resource allocations for Azure Machine Learning compute instances with workspace- and resource-level quota limits.
See Azure Machine Learning pricing
Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes.
Many people working with data have developed skills in math, programming, or domain expertise, but proper data science calls for all three. This comprehensive e-book helps fill in the gaps.
Forrester names Microsoft Azure Machine Learning a leader in The Forrester Waveâ„¢: Notebook-Based Predictive Analytics And Machine Learning, Q3 2020.
Go to your studio web experience
Build and train
Deploy and manage
Author new models and store your compute targets, models, deployments, metrics, and run histories in the cloud.
Use automated machine learning to identify algorithms and hyperparameters and track experiments in the cloud. Author models using notebooks or the drag-and-drop designer.
Deploy your machine learning model to the cloud or the edge, monitor performance, and retrain it as needed.
Get instant access and a $200 credit by signing up for an Azure free account.
Sign in to the Azure portal.
Explore the documentation and tutorials, and browse quickstarts and developer resources.
"The model we deployed on Azure Machine Learning helped us choose the three new retail locations we opened in 2019. Those stores exceeded their revenue plans by over 200 percent in December, the height of our season, and within months of opening were among the best-performing stores in their districts."
"MLOps is at the core of our product. Because of its reproducible ML pipelines, ... registered models, and automatic model scoring, we're definitely detecting things that we missed before. Which, in terms of risk management, is really, really important."
"Azure Machine Learning allows us to manage the entire lifecycle, from experimentation and development to production and enhancements."
"With model interpretability in Azure Machine Learning, we have a high degree of confidence that our machine learning model is generating meaningful and fair results."
"If I have 200 models to train—I can just do this all at once. It can be farmed out to a huge compute cluster, and it can be done in minutes. So I'm not waiting for days."
"We see Azure Machine Learning and our partnership with Microsoft as critical to driving increased adoption and acceptance of AI from the regulators."
ANNOUNCEMENT
UPDATE