Azure Machine Learning

Servicio de aprendizaje automático de nivel empresarial para crear e implementar modelos con más rapidez

Agilice el ciclo de vida completo del aprendizaje automático

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 integral

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.

Abierto e interoperable

La mayor compatibilidad con plataformas y lenguajes de código abierto, como MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python y 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.

Ponga en práctica los modelos a gran escala con 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.

Innove en una plataforma abierta y flexible

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.

Seguridad avanzada y gobernanza

  • 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.
  • Administre la gobernanza con directivas, trazas de seguimiento, cuotas y administración de costos.
  • Optimice el cumplimiento normativo con una cartera completa que incluye 60 certificaciones, incluidas FedRAMP High y DISA IL5.

Principales características del servicio

Cuadernos de colaboración

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.

Etiquetado de datos

Prepare los datos rápidamente, administre y supervise proyectos de etiquetado y automatice tareas iterativas con el etiquetado asistido por aprendizaje automático.

MLOps

Use el registro central para almacenar y hacer un seguimiento de los datos, los modelos y los metadatos. Capture automáticamente datos de linaje y gobernanza. Use Git para hacer un seguimiento del trabajo, y Acciones de GitHub, para implementar flujos de trabajo. Administre y supervise las ejecuciones o compare varias ejecuciones para entrenamiento y experimentación.

Proceso con escalabilidad automática

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).

Integración profunda con otros servicios de Azure

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.

Reforzar el aprendizaje

Scale reinforcement learning to powerful compute clusters, support multi-agent scenarios, and access open-source reinforcement learning algorithms, frameworks, and environments.

Responsible machine learning

Obtenga transparencia en los modelos durante el entrenamiento y la inferencia con las características de interpretabilidad. Valore la imparcialidad de los modelos usando métricas de disparidad y mitigue la parcialidad. Proteja los datos con privacidad diferencial.

Seguridad de clase empresarial

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.

Administración de costos

Better manage resource allocations for Azure Machine Learning compute instances with workspace- and resource-level quota limits.

Pague solo por lo que necesita, sin costos por adelantado

See Azure Machine Learning pricing.

Dominio de Azure Machine Learning

Domine las técnicas expertas para crear modelos y canalizaciones de aprendizaje automático de un extremo a otro automatizados y altamente escalables en Azure con TensorFlow, Spark y Kubernetes.

Packt: Principios de la ciencia de datos

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.

Líder en 2020 en Forrester Wave

Forrester reconoce a Microsoft como líder por Azure Machine Learning en el informe The Forrester Wave™: análisis predictivo y aprendizaje automático basados en cuadernos, 3er trimestre de 2020.

Cómo se utiliza Azure Machine Learning

Viva una experiencia web con Studio

Compilación y entrenamiento

Implementación y administración

Paso 1 de 1

Author new models and store your compute targets, models, deployments, metrics, and run histories in the cloud.

Paso 1 de 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.

Paso 1 de 1

Implemente su propio modelo de Machine Learning en la nube o en el perímetro, supervise el rendimiento y vuelva a entrenarlo según sea necesario.

Comience a usar Azure Machine Learning hoy mismo

Consiga acceso inmediato y un crédito por valor de $200 al registrarse para obtener una cuenta gratuita de Azure.

Inicie sesión en Azure Portal.

Clientes que utilizan 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: directora de inteligencia empresarial y análisis, Carhartt
Carhartt

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.

Scandinavian Airlines

"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, científico de datos sénior, Análisis global, Walgreens Boots Alliance
Walgreens Boots Alliance

"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: asociado y asesor jefe de inteligencia artificial, análisis y datos, EY Canada
EY

"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."

Xiaodong Wang, CEO, TalentCloud
TalentCloud

Actualizaciones, blogs y anuncios de Azure Machine Learning

Preguntas más frecuentes de Azure Machine Learning

  • El servicio está disponible con carácter general en varios países y regiones, y se agregarán más próximamente.
  • El Acuerdo de Nivel de Servicio de Azure Machine Learning es del 99,9 %.
  • Azure Machine Learning Studio es el principal recurso de Machine Learning Service. Proporciona un lugar centralizado para que los científicos de datos y desarrolladores trabajen con todos los artefactos para crear, entrenar e implementar modelos de Machine Learning.

Cuando quiera, podemos configurar su cuenta gratuita de Azure