Microsoft Azure Machine Learning as a Service: Overview

Source: Microsoft

Machine Learning delivers predictive Intelligence and accurate outcomes to stay competitive in the market. But many companies find it difficult to adopt machine learning solution due to monotonous task, slow training, and complex deployment. They need tools to accelerate and automate their machine learning project lifecycle. Microsoft presents its Azure Machine Learning to remove these obstacles by accelerating machine learning life cycle from end to end. It is collection of services and tools created to help Data Scientist and developers to train and deploy machine learning models through its Azure Public Cloud. Microsoft Azure is widely considered both Platform as a Service and Infrastructure as a Service. It’s a drag and drop machine learning that lets work your idea to deployment with no additional setup required. This automated machine learning handles the critical Feature Engineering, Algorithm Selection and Hyperparameter Tuning itself to ease the process.

It supports many open-source framework such as OONX, PyTorch, SciKit-learn, TensorFlow that delivers compatibility of notebooks like PyCharm, VSCode, Jupiter Notebooks to optimize and accelerate inferencing across cloud and edge devices. It allows you to use your own set or R or Python tools. With Microsoft Azure ML you can build pipelines which let you share N2 and data science experiments optimizing the day-to-day workflows. It boosts your productivity for all skill level by building and deploying machine learning models using the inbuilt tools and improving your model quality automatically overtime with Integrated CI/CD. It provides you with enterprise-grade security so that you can innovate freely.

Microsoft Azure accelerate productivity with built in Integration with Microsoft Power BI and Azure services such as Azure Synapse Analytics, Azure Data Factory and Azure Data Lake. The Microsoft Azure ML supports array of tools and services such as

Azure Machine Learning Workbench:  Interoperates with major third-party tools for version control, Data Cleansing and transformation.

Azure machine learning Experimentation Service:  Interoperates with Workbench helping in the execution of machine learning experiments to build and train models.

Azure Machine learning Model management: helps developers to track and manage model versions.

MMLSpark: It provides a series of tools that Integrate spark pipelines with related machine learning tools.

Visual Studio Code tool for AI: It helps developers create script and gather metrics for Azure Machine Learning Experiments.

Azure Machine Learning studio: Drag and drop tool designed to help user with no coding knowledge for building and deploying models.

 

Source:(https://azure.microsoft.com/en-in/services/machine-learning/#features)

(https://medium.com/@ahmedkhemiri24/microsoft-azure-machine-learning-d148478e867c)

 

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