Azure Machine Learning
Maskininlärningstjänst i företagsklass som bygger och distribuerar modeller snabbare
Snabbare livscykel för maskininlärning från slutpunkt till slutpunkt
The Azure Machine Learning service empowers developers and data scientists with a wide range of productive experiences for building, training, and deploying machine learning models faster. Accelerate time to market and foster team collaboration with industry-leading MLOps—DevOps for machine learning. Innovate on a secure, trusted platform, designed for responsible machine learning.
Machine learning for all skills
Productivity for all skill levels, with code-first and drag-and-drop designer, and automated machine learning.
MLOps från slutpunkt till slutpunkt
Robust MLOps capabilities that integrate with existing DevOps processes and help manage the complete machine learning lifecycle.
State-of-the-art responsible machine learning
Responsible machine learning capabilities—understand models with interpretability and fairness, protect data with differential privacy and confidential computing, and control the machine learning lifecycle with audit trials and datasheets.
Öppen och kompatibel
Förstklassig support för ramverk med öppen källkod och språk, bland annat MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python och R.
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 the no-code designer to get started with visual machine learning or built-in collaborative Jupyter Notebooks for a code-first experience. Accelerate model creation with automated machine learning, and access built-in feature engineering, algorithm selection, and hyperparameter sweeping to develop highly accurate models.
Operationalisera i stor skala med MLOps
MLOps, or DevOps for machine learning, streamlines the machine learning lifecycle, from building models to deployment and management. Use machine learning pipelines to build repeatable workflows, and use a rich model registry to track your assets. Manage production workflows at scale using advanced alerts and machine learning automation capabilities. Profile, validate, and deploy machine learning models anywhere, from the cloud to the edge, to manage production machine learning workflows at scale in an enterprise-ready fashion.
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.
Skapa på en öppen och flexibel plattform
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.
Avancerad säkerhet och styrning
- Get end-to-end security and build on the trusted cloud with Azure.
- Protect your resources with granular role-based access, custom roles, and built-in mechanisms for identity authentication.
- Build, train, and deploy models more securely by isolating your network with virtual networks and private links.
- Hantera styrning med principer, granska utvärderingar, kvot- och kostnadshantering.
- Effektivisera kompatibiliteten med en omfattande portfölj som sträcker sig över 60 certifieringar, inklusive FedRAMP High och DISA IL5.
Viktig tjänstfunktioner
Samarbetsbaserade notebook-filer
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.
Dataetikettering
Förbered data snabbt, hantera och övervaka etiketteringsprojekt och automatisera repetitiva uppgifter med hjälp av maskininlärningsassisterad etikettering.
MLOps
Använd det centrala registret för att lagra och spåra data, modeller och metadata. Samla automatiskt in ursprungs- och styrningsdata. Använd Git till att spåra arbete och GitHub Actions till att implementera arbetsflöden. Hantera och övervaka körningar eller jämför flera körningar för träning och experimentering.
Beräkning med automatisk skalning
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.
RStudio support
Build and deploy models and monitor runs with built-in R support and RStudio Server (open source edition).
Djup integrering med andra Azure-tjänster
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, and Azure Databricks.
Kunskapsförmedling
Scale reinforcement learning to powerful compute clusters, support multi-agent scenarios, and access open-source reinforcement learning algorithms, frameworks, and environments.
Responsible machine learning
Få modelltransparens vid träning och slutsatsdragning med funktioner för tolkningsbarhet. Utvärdera modellrättvisa via diskrepansmått och minska orättvisa. Skydda data med differentiell sekretess.
Säkerhet i företagsklass
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.
Kostnadshantering
Better manage resource allocations for Azure Machine Learning compute instances with workspace- and resource-level quota limits.
Betala endast för det du behöver utan några startkostnader
Lär dig mer om Azure Machine Learning
Lär dig att använda experttekniker för att skapa automatiserade och mycket skalbara kompletta maskininlärningsmodeller och pipelines i Azure med TensorFlow, Spark och Kubernetes
Packt: Datavetenskapens principer
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 Wave-ledare 2020
Forrester utser Microsoft och Azure Machine Learning till ledare i The Forrester Wave™ inom Notebook-baserad förutsägelseanalys och maskininlärning för kvartal 3 2020.
Använda Azure Machine Learning
Gå till din studiowebb
Skapa och träna
Distribuera och hantera
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.
Distribuera din maskininlärningsmodell till molnet eller gränsenheter, övervaka prestandan och träna upp modellen på nytt vid behov.
Resurser
Självstudier för nybörjare
Avancerade självstudier
Additional resources
Börja använda Azure Machine Learning i dag
Få direktåtkomst och en kredit på $200 genom att registrera dig för ett kostnadsfritt Azure-konto.
Logga in på Azure-portalen.
Utforska dokumentation och självstudier. Hitta snabbstarter och utvecklarresurser.
Kunder som använder Azure Machine Learning
Jolie Vitale: Director of BI and Analytics på Carhartt"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."
By using Azure Machine Learning, Scandinavian Airlines (SAS) is accurately identifying fraud with proficiency that wasn’t possible through manual methods. In the case of retroactively registering a flight for EuroBonus miles—a common source of fraud—the new system predicts fraud with 99 percent accuracy.
Sze-Wan Ng: Director of Analytics & Development på Translink"With MLOps capabilities in Azure Machine Learning, we've improved bus departure predictions by 74 percent, and riders spend 50 percent less time waiting."
Dean Riddlesden, Senior Data Scientist, Global Analytics, Walgreens Boots Alliance"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."
Alex Mohelsky: Partner and Advisory Data, Analytic, and AI Leader på EY Canada"We see Azure Machine Learning and our partnership with Microsoft as critical to driving increased adoption and acceptance of AI from the regulators."
Xiaodong Wang, CEO, TalentCloud"The automated machine learning capabilities in Azure Machine Learning save our data scientists from doing a lot of time-consuming work, which reduces our time to build models from several weeks to a few hours."
Uppdateringar, bloggar och meddelanden om Azure Machine Learning
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TILLKÄNNAGIVANDE
Azure Open Datasets finns nu i förhandsversion och ger dig tillgång till utvalda datamängder.
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UPPDATERING
Azure Machine Learning updates December 2020 in public preview
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UPPDATERING
Azure Machine Learning updates--November 2020
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UPPDATERING
Azure Machine Learning offers added capabilities at lower cost
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UPPDATERING
Azure Machine Learning updates Ignite 2020
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UPPDATERING
Azure Machine Learning announces output dataset (Preview)
Vanliga frågor och svar om Azure Machine Learning
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Tjänsten är allmänt tillgänglig i flera länder/regioner och fler är på gång.
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Serviceavtalet (SLA) för Azure Machine Learning är 99,9 procent.
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Azure Machine Learning-studion är tjänstens toppnivåresurs för maskininlärning. Den är en central plats för dataforskare och utvecklare där de kan arbeta med alla artefakter och skapa, träna och distribuera maskininlärningsmodeller.