Develop Clustering Models with Azure ML Designer

제공자:
Microsoft
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

Create an Azure Machine Learning Workspace using the Azure Portal

Develop a Clustering Model in Azure ML Designer

Publish the model for application use

Clock2 Hours
Intermediate중급
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

This is an intermediate project on creating clustering models in Azure Machine Learning Studio. Familiarity with any Web Browser and navigating Windows Desktop is assumed. Some background knowledge on Machine Learning or Cloud computing is beneficial but not required to complete this project. Understanding how platform services in the cloud work and how machine learning algorithms function would be of great help in understanding better what we are executing in this guided project. Some minimal data engineering and data scientist knowledge is required. This guided project has the aim to demonstrate how you can create Machine Learning models by using the out-of-the-box solutions that Azure offers, by just using these services as-is, on your own data. The main focus is on the data and how this is being used by the services. As this project is based on Azure technologies, an Azure subscription is required. The project also outlines a step where an Azure subscription will be created and for this, the following items are required: a valid phone number, a credit card, and a GitHub or Microsoft account username. The series of tasks will mainly be carried out using a web browser. If you enjoy this project, we recommend taking the Microsoft Azure AI Fundamentals AI-900 Exam Prep Specialization: https://www.coursera.org/specializations/microsoft-azure-ai-900-ai-fundamentals

개발할 기술

Artificial Intelligence (AI)Machine LearningCloud Computingclustering

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  1. Create a free trial account in Microsoft Azure and log into Azure using your new subscription.

  2. Create a Resource Group in preparation for creating a new Azure Machine Learning Workspace.

  3. Create an Azure Machine Learning Workspace to manage artifacts related to your machine learning workloads.

  4. Create compute targets on which to run the training process.

  5. Create a dataset and explore data.

  6. Create a pipeline in Azure Machine Learning Designer.

  7. Apply data transformations to cluster observations.

  8. Add training modules and apply a clustering algorithm.

  9. Run the training pipeline to train the model.

  10. Evaluate the clustering model by using the Evaluate Model module.

  11. Create an inference pipeline to assign new data observations.

  12. Publish the predictive service for application use.

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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