Berta Escamilla

Berta Escamilla

1649318400

Azure Machine Learning: Implementación Del Modelo

En este artículo, aprenderemos a implementar nuestro modelo de aprendizaje automático mediante la funcionalidad de código bajo que se ofrece a través de Designer en Azure Machine Learning. Pasaremos por un proceso paso a paso para implementar nuestro modelo de aprendizaje automático para numerosos servicios y aplicaciones mediante la canalización de inferencia disponible en Azure Machine Learning. Crearemos una tubería de inferencia en tiempo real y, por lo tanto, a través del punto final, nuestro modelo ML se puede usar para predecir a partir de los datos de entrada. La aplicación externa tomará la entrada y enviará los datos a nuestro modelo ML a través del punto final y una vez que nuestro modelo ML procese y proporcione la salida prevista, se envía de vuelta y la salida se puede mostrar o usar en cualquier aplicación. Este artículo forma parte de la serie Azure Machine Learning.

  1. Azure Machine Learning: creación de un espacio de trabajo para el aprendizaje automático
  2. Azure Machine Learning: creación de una instancia informática y un clúster informático
  3. Aprendizaje automático de Azure: escritura de secuencias de comandos de Python en Notebook
  4. Aprendizaje automático de Azure: entrenamiento de modelos
  5. Azure Machine Learning: regresión lineal
  6. Azure Machine Learning: implementación del modelo

Requisito previo

Este artículo es una continuación de los artículos anteriores, Azure Machine Learning: creación de un área de trabajo para el aprendizaje automático, Azure Machine Learning: creación de una instancia informática y un clúster informático y Azure Machine Learning: regresión lineal en orden.

Entremos en el proceso paso a paso usando el diseñador para implementar nuestro modelo de Machine Learning en Azure Machine Learning.

Paso 1

Una vez que haya ejecutado el modelo de regresión lineal , el lienzo debe tener un aspecto similar al siguiente. Todos los componentes serían verdes con la nota Completado.

Creación de canalización de inferencia

Paso 2

Para implementar nuestra canalización de aprendizaje automático, es esencial convertir la canalización de entrenamiento en una canalización de inferencia en tiempo real.

Ahora vamos a crear una canalización de inferencia en tiempo real. Primero, haga clic en Crear canalización de inferencia y luego en Canalización de inferencia en tiempo real.

Paso 3

Podemos ver que los componentes de capacitación se han eliminado y se han agregado componentes de entrada y salida del servicio web.

Haga clic en Enviar.

Necesitamos seleccionar nuestro experimento bajo nuestro objetivo de cómputo. Aquí, el mío es aprender-regresión lineal. También agregué la descripción del trabajo, que será útil cuando lo analicemos más adelante.

Seleccione el paso Continuar en caso de error y haga clic en Enviar.

Etapa 4

Podemos ver el proceso de ejecución.

Una vez que se completa el proceso de ejecución, podemos ver la nota de la competencia.

Podemos ver la diferencia entre el Canal de entrenamiento y el Canal de inferencia en tiempo real al cambiar el lienzo desde el Menú.

Despliegue

Paso 5

Ahora, encima del lienzo de canalización de inferencia en tiempo real, haga clic en Implementar.

Podemos ver los detalles a rellenar. Bajo Compute Name, no podemos seleccionar ningún recurso. Esto se debe a que Compute Instances no se puede usar para la implementación. Necesitaríamos un clúster de inferencia.

Vayamos a la página de cálculo. Aquí, haga clic en Clústeres de inferencia.

Haga clic en + Botón Nuevo.

Paso 6

Aquí, en Máquina virtual para el servicio de Kubernetes, haga clic en Crear nuevo y seleccione la ubicación.

A medida que selecciono el centro de EE. UU., podemos ver las diferentes opciones.

Optemos por Standard_A2_v2 ya que 2 núcleos, 4 GB de RAM y 20 GB de almacenamiento serían suficientes para nosotros.

Paso 7

Ahora, haga clic en Siguiente o Configuración avanzada.

Asigne un nombre a su cálculo. Aquí he nombrado mi ojash-akscompute. Establezca el propósito del clúster en producción: esto lo configurará para el entorno de producción que podemos usar para el uso en el mundo real. El número de nodos se puede establecer en 3. Establezca la Configuración de red en Básica y luego haga clic en Crear.

Podemos ver el estado del clúster. Cuando comienza el proceso, podemos ver que está en estado de creación.

A medida que se complete, el estado se mostrará como Sucedida.

Podemos ver los detalles haciendo clic en él.

Paso 8

Ahora, como hemos creado nuestro clúster de inferencia, procedamos con el botón Implementar en la canalización de inferencia en tiempo real.

Asigne un nombre a su punto final en tiempo real. Aquí he llamado al mío ojashlrmodel-deploy.

El tipo de proceso sería Azure Kubernetes Service y seleccione el proceso que ha creado, que es el clúster de inferencia de servicio de Kubernetes.

Realice los cambios necesarios comprobando la configuración avanzada para el tiempo de puntuación, los números de reemplazo y la utilización objetivo según sus necesidades.

Finalmente, haga clic en Implementar.

Paso 9

Comienza el proceso de implementación y se nos notifica al respecto.

A medida que se ejecuta el proceso, podemos ver el estado de implementación como Transición.

Una vez completado, podemos obtener el REST Endpoint y la URL de Swagger.

Paso 10

A continuación, tendremos cuatro pestañas que incluyen Detalles, Prueba, Consumir y Registros de implementación.

La página de detalles contiene los detalles del servicio de implementación.

La página de consumo consta del punto final REST, las claves de autenticación, tanto primarias como secundarias. Además, también tenemos los códigos de consumo según la plataforma para C#, Python y R.

Los registros de implementación consisten en todos los registros de la implementación y el uso de la aplicación externa del modelo y los servicios de ML.

Paso 11

La página de prueba consta de los datos de entrada que se probarán para el punto final en tiempo real.

Al hacer clic en el botón de prueba, podemos obtener el resultado de la prueba.

Datos de entrada para probar el punto final en tiempo real

{
  "Inputs": {
    "WebServiceInput0": [
      {
        "symboling": 3,
        "normalized-losses": 1.0,
        "make": "alfa-romero",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "two",
        "body-style": "convertible",
        "drive-wheels": "rwd",
        "engine-location": "front",
        "wheel-base": 88.6,
        "length": 168.8,
        "width": 64.1,
        "height": 48.8,
        "curb-weight": 2548,
        "engine-type": "dohc",
        "num-of-cylinders": "four",
        "engine-size": 130,
        "fuel-system": "mpfi",
        "bore": 3.47,
        "stroke": 2.68,
        "compression-ratio": 1.0,
        "horsepower": 1.0,
        "peak-rpm": 1.0,
        "city-mpg": 21,
        "highway-mpg": 27,
        "price": 1.0
      },
      {
        "symboling": 3,
        "normalized-losses": 1.0,
        "make": "alfa-romero",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "two",
        "body-style": "convertible",
        "drive-wheels": "rwd",
        "engine-location": "front",
        "wheel-base": 88.6,
        "length": 168.8,
        "width": 64.1,
        "height": 48.8,
        "curb-weight": 2548,
        "engine-type": "dohc",
        "num-of-cylinders": "four",
        "engine-size": 130,
        "fuel-system": "mpfi",
        "bore": 3.47,
        "stroke": 2.68,
        "compression-ratio": 1.0,
        "horsepower": 1.0,
        "peak-rpm": 1.0,
        "city-mpg": 21,
        "highway-mpg": 27,
        "price": 1.0
      },
      {
        "symboling": 1,
        "normalized-losses": 1.0,
        "make": "alfa-romero",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "two",
        "body-style": "hatchback",
        "drive-wheels": "rwd",
        "engine-location": "front",
        "wheel-base": 94.5,
        "length": 171.2,
        "width": 65.5,
        "height": 52.4,
        "curb-weight": 2823,
        "engine-type": "ohcv",
        "num-of-cylinders": "six",
        "engine-size": 152,
        "fuel-system": "mpfi",
        "bore": 2.68,
        "stroke": 3.47,
        "compression-ratio": 1.0,
        "horsepower": 1.0,
        "peak-rpm": 1.0,
        "city-mpg": 19,
        "highway-mpg": 26,
        "price": 1.0
      },
      {
        "symboling": 2,
        "normalized-losses": 1.0,
        "make": "audi",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "four",
        "body-style": "sedan",
        "drive-wheels": "fwd",
        "engine-location": "front",
        "wheel-base": 99.8,
        "length": 176.6,
        "width": 66.2,
        "height": 54.3,
        "curb-weight": 2337,
        "engine-type": "ohc",
        "num-of-cylinders": "four",
        "engine-size": 109,
        "fuel-system": "mpfi",
        "bore": 3.19,
        "stroke": 3.4,
        "compression-ratio": 1.0,
        "horsepower": 1.0,
        "peak-rpm": 1.0,
        "city-mpg": 24,
        "highway-mpg": 30,
        "price": 1.0
      },
      {
        "symboling": 2,
        "normalized-losses": 1.0,
        "make": "audi",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "four",
        "body-style": "sedan",
        "drive-wheels": "4wd",
        "engine-location": "front",
        "wheel-base": 99.4,
        "length": 176.6,
        "width": 66.4,
        "height": 54.3,
        "curb-weight": 2824,
        "engine-type": "ohc",
        "num-of-cylinders": "five",
        "engine-size": 136,
        "fuel-system": "mpfi",
        "bore": 3.19,
        "stroke": 3.4,
        "compression-ratio": 1.0,
        "horsepower": 1.0,
        "peak-rpm": 1.0,
        "city-mpg": 18,
        "highway-mpg": 22,
        "price": 1.0
      }
    ]
  },
  "GlobalParameters": {}
}

YAML

Dupdo

Aquí, podemos obtener la etiqueta de puntuación para un nuevo conjunto de datos de entrada.

Resultado de la prueba

{
  "Results": {
    "WebServiceOutput0": [
      {
        "symboling": 3,
        "make": "alfa-romero",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "two",
        "body-style": "convertible",
        "drive-wheels": "rwd",
        "engine-location": "front",
        "wheel-base": 88.6,
        "length": 168.8,
        "width": 64.1,
        "height": 48.8,
        "curb-weight": 2548,
        "engine-type": "dohc",
        "num-of-cylinders": "four",
        "engine-size": 130,
        "fuel-system": "mpfi",
        "bore": 3.47,
        "stroke": 2.68,
        "compression-ratio": 1,
        "horsepower": 1,
        "peak-rpm": 1,
        "city-mpg": 21,
        "highway-mpg": 27,
        "price": 1,
        "Scored Labels": 2514.163465984617
      },
      {
        "symboling": 3,
        "make": "alfa-romero",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "two",
        "body-style": "convertible",
        "drive-wheels": "rwd",
        "engine-location": "front",
        "wheel-base": 88.6,
        "length": 168.8,
        "width": 64.1,
        "height": 48.8,
        "curb-weight": 2548,
        "engine-type": "dohc",
        "num-of-cylinders": "four",
        "engine-size": 130,
        "fuel-system": "mpfi",
        "bore": 3.47,
        "stroke": 2.68,
        "compression-ratio": 1,
        "horsepower": 1,
        "peak-rpm": 1,
        "city-mpg": 21,
        "highway-mpg": 27,
        "price": 1,
        "Scored Labels": 2514.163465984617
      },
      {
        "symboling": 1,
        "make": "alfa-romero",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "two",
        "body-style": "hatchback",
        "drive-wheels": "rwd",
        "engine-location": "front",
        "wheel-base": 94.5,
        "length": 171.2,
        "width": 65.5,
        "height": 52.4,
        "curb-weight": 2823,
        "engine-type": "ohcv",
        "num-of-cylinders": "six",
        "engine-size": 152,
        "fuel-system": "mpfi",
        "bore": 2.68,
        "stroke": 3.47,
        "compression-ratio": 1,
        "horsepower": 1,
        "peak-rpm": 1,
        "city-mpg": 19,
        "highway-mpg": 26,
        "price": 1,
        "Scored Labels": 4169.688613458313
      },
      {
        "symboling": 2,
        "make": "audi",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "four",
        "body-style": "sedan",
        "drive-wheels": "fwd",
        "engine-location": "front",
        "wheel-base": 99.8,
        "length": 176.6,
        "width": 66.2,
        "height": 54.3,
        "curb-weight": 2337,
        "engine-type": "ohc",
        "num-of-cylinders": "four",
        "engine-size": 109,
        "fuel-system": "mpfi",
        "bore": 3.19,
        "stroke": 3.4,
        "compression-ratio": 1,
        "horsepower": 1,
        "peak-rpm": 1,
        "city-mpg": 24,
        "highway-mpg": 30,
        "price": 1,
        "Scored Labels": 1223.6365517221493
      },
      {
        "symboling": 2,
        "make": "audi",
        "fuel-type": "gas",
        "aspiration": "std",
        "num-of-doors": "four",
        "body-style": "sedan",
        "drive-wheels": "4wd",
        "engine-location": "front",
        "wheel-base": 99.4,
        "length": 176.6,
        "width": 66.4,
        "height": 54.3,
        "curb-weight": 2824,
        "engine-type": "ohc",
        "num-of-cylinders": "five",
        "engine-size": 136,
        "fuel-system": "mpfi",
        "bore": 3.19,
        "stroke": 3.4,
        "compression-ratio": 1,
        "horsepower": 1,
        "peak-rpm": 1,
        "city-mpg": 18,
        "highway-mpg": 22,
        "price": 1,
        "Scored Labels": 2959.4884974132037
      }
    ]
  }
}

YAML

Dupdo

Esto muestra cómo nuestro modelo de aprendizaje automático es capaz de obtener datos de entrada y procesar para proporcionar Predicción basada en la entrada a través del punto final desde cualquier aplicación web. Ahora hemos implementado con éxito nuestro modelo de aprendizaje automático mediante Inference Pipeline.

Eliminación de recursos 

Paso 12

Para evitarnos cargos innecesarios en los que incurrir, es una buena práctica eliminar todos los grupos de recursos una vez que nuestras tareas se hayan completado o no sean de uso prolongado. En lugar de eliminar los recursos uno por uno, lo que lleva más tiempo, visitemos Azure Portal y eliminemos todo el grupo de recursos.

Haga clic en Eliminar grupo de recursos y vuelva a escribir el nombre del grupo de recursos y finalmente presione el botón Eliminar.

Conclusión

Por lo tanto, en este artículo, pasamos por un proceso paso a paso para implementar un modelo de aprendizaje automático listo para usar en cualquier otra plataforma o aplicación web a través de un punto final en tiempo real que creamos a través de la canalización de inferencia e implementamos a través del servicio Kubernetes.

Fuente: https://www.c-sharpcorner.com/article/azure-machine-learning-model-deployment/ 

#azure #machine-learning 

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Azure Machine Learning: Implementación Del Modelo
sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Nora Joy

1604154094

Hire Machine Learning Developers in India

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.
**
Services**
Product Engineering & Development
Re-engineering
Maintenance / Support / Sustenance
Integration / Data Management
QA & Automation
Reach us 917483546629

Hire machine learning developers in India ,DxMinds Technologies is the best product engineering company in India making innovative solutions using Machine learning and deep learning. We are among the best to hire machine learning experts in India work in different industry domains like Healthcare retail, banking and finance ,oil and gas, ecommerce, telecommunication ,FMCG, fashion etc.

Services

Product Engineering & Development

Re-engineering

Maintenance / Support / Sustenance

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Reach us 917483546629

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Nora Joy

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Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers

Nora Joy

1607006620

Hire Machine Learning Developer | Hire ML Experts in India

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning
Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Layla Gerhold

Layla Gerhold

1597160392

Azure Machine Learning Service

In a series of blog posts, I am planning to write down my experiences of training, deploying and managing models and running pipelines with Azure Machine Learning Service. This is part-1 where I will be walking you through the creation of workspace in Azure ML service

About Azure Machine Learning Service

Azure Machine Learning Service is a cloud based platform from Microsoft to train, deploy, automate, manage and track ML models. It has a facility to build models by using drag-drop components in Designer along with traditional code based model building. Azure ML service makes our job very ease in maintaining developed models and also helps in hassle free deployment of models in lower(QA, Unit) and higher(Prod) environments as APIs. It is integrated with various components in Azure like Azure Kubernetes Services, **Azure Databricks, Azure Monitor, Azure Storage accounts, Azure Pipelines, MLFlow, Kubeflow **to carry out various activities which will be discussed in upcoming posts.

Why Azure Machine Learning Service

In the process of building models, one need to play around with various hyperparameters and use various techniques. Also one need to scale out the resources for training the model if the dataset is huge. Bringing your model development and deployment to cloud makes your job easy. In particular Azure Machine Learning Service has below advantages.

  1. Simplifies model management
  2. Automated machine learning simplifies model building
  3. Scales out training to GPU cluster or CPU cluster or Azure Databricks whenever needed with inbuilt integration
  4. Deployment of models to production with Azure Kubernetes Service or Azure IOT edge is very simple.

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