Azure Machine Learning Studio
TLDRThis tutorial demonstrates the use of Azure Machine Learning Studio for no-code and low-code development. It covers creating a workspace, utilizing resources like storage accounts and key vaults, and navigating the ML studio interface. The process includes data cleaning, feature selection, model training with a boosted decision tree, and evaluation. The video also explains deploying models as endpoints for applications to consume. Additionally, it showcases automated ML for simplified model selection and deployment. The presenter guides through creating compute clusters, registering models, and testing the deployed endpoints with sample codes in C#, Python, and R.
Takeaways
- 🚀 **Azure ML Workspace Creation**: Start by creating an Azure Machine Learning workspace, which is essential for managing resources and activities.
- 📋 **Resource Group Utilization**: Organize resources using a resource group, which aids in billing and management, and can be deleted as a set.
- 🌐 **Region Selection**: Choose a region close to your target audience for optimal performance and reduced latency.
- 🧠 **Azure ML Studio Overview**: Azure ML Studio provides a portal for no-code and low-code development of machine learning models.
- 🔍 **Designer and Automated ML**: Utilize the Designer for visual model building and Automated ML for quick model generation without extensive coding.
- 📊 **Data Handling**: Store datasets in the designated storage account, manage keys in the key vault, and monitor with Application Insights.
- 🔧 **Pipeline Development**: Develop a sequence of activities (a pipeline) for machine learning tasks, including data cleaning, feature selection, and model training.
- 📈 **Model Training and Evaluation**: Train models with a chosen algorithm, split data for training and validation, and evaluate model performance.
- 💻 **Compute Resource Management**: Use clusters for scalable compute resources, which auto-scale and can reduce costs when not in use.
- 🔗 **Endpoint Deployment**: Deploy trained models as endpoints or web services, making them accessible for applications to consume.
- 📝 **Model Registration and Consumption**: Register models for reuse and deployment, and consume the endpoints with provided sample codes for different programming languages.
Q & A
What is the first step in using Azure Machine Learning Studio?
-The first step is to create an Azure Machine Learning workspace.
What is the purpose of a resource group in Azure?
-A resource group is a logical organization of your resources for billing purposes and resource management, allowing you to manage and delete them as a group.
How does Azure Machine Learning Studio differ from Machine Learning Classic?
-Azure Machine Learning Studio is the enterprise version, designed for no-code and low-code development, while Machine Learning Classic is an older version.
What are the main resources created under a workspace in Azure Machine Learning?
-The main resources created under a workspace include a storage account, application insights, and a key vault for storing keys.
How does the automated ML feature in Azure Machine Learning Studio work?
-Automated ML allows users to select a dataset and label columns, and the system automatically chooses the best algorithm and provides a machine learning model without the need for extensive programming.
What does the Designer feature in Azure Machine Learning Studio offer?
-The Designer feature is for data scientists who are familiar with the process but do not want to spend time coding everything. It allows for a visual, drag-and-drop interface to create machine learning models.
How can data cleaning be approached in Azure Machine Learning Studio?
-Data cleaning can be done using components like 'Clean Missing Data', where you can choose to replace missing values with means or other statistical measures.
What is the role of the 'Compute' resource in Azure Machine Learning?
-The 'Compute' resource allows you to manage your computing resources under Azure, which are used for training and deploying machine learning models.
How does one deploy a machine learning model as a web service in Azure Machine Learning Studio?
-After training and registering the model, you can deploy it as a web service by creating an endpoint, selecting the compute resource, and providing the necessary scripts and environment files.
What are the two main approaches demonstrated for deploying machine learning models in Azure Machine Learning Studio?
-The two main approaches are deploying models using the Designer, which involves creating a pipeline, training a model, and then deploying it, and deploying models using Automated ML, which automates the process of selecting the best model and deploying it as an endpoint.
How can developers test and consume the endpoints or web services published from Azure Machine Learning Studio?
-Developers can test the API endpoints using the 'Test' tab, which allows for form format testing and JSON editor testing. The 'Consume' tab provides the URL and sample codes in C#, Python, or R for easy integration into applications.
Outlines
🚀 Introduction to Azure Machine Learning Workspace
The video begins with an introduction to Azure Machine Learning and the process of setting up a workspace for no-code or low-code development. The presenter demonstrates how to access cloud services, create a resource group named RG001 in the Southeast Asia region, and review the resources created. The focus is on the logical organization of resources for billing and management purposes. The presenter also explains the creation of a machine learning workspace named WS001, which includes a storage account, application insights, and a key vault for security keys.
📚 Exploring ML Studio and Automated ML
The second paragraph delves into the features of Azure Machine Learning Studio, including the ability to perform machine learning or data science activities in notebooks or through automated ML for those with limited programming skills. The presenter discusses the various assets such as data, pipelines, experiments, models, and endpoints that a data scientist works with. It also covers the process of using the Designer for data scientists who prefer a visual interface over coding. The presenter guides through accessing the Titanic dataset from a GitHub URL and preparing it for use in the ML studio.
🔍 Data Cleaning and Feature Selection
In this section, the presenter discusses the importance of data cleaning and feature selection in the machine learning pipeline. They demonstrate how to clean missing data by replacing it with the mean value and how to manually select relevant features for the model. The presenter also touches on the option to write custom scripts in R or Python if needed, emphasizing the no-code and low-code capabilities of the platform.
🤖 Model Training and Evaluation
The presenter explains the process of training a machine learning model using a chosen algorithm, in this case, a boosted decision tree for binary classification. They describe how to split the data into training and validation sets, train the model, and then evaluate its performance. The paragraph also highlights the need to select a compute resource for running the pipeline and the presenter's decision to use a cluster for its auto-scaling capabilities.
🤖 Automated Machine Learning Process
The focus shifts to automated machine learning, where the presenter outlines the steps to set up and run an automated ML job. They discuss choosing a dataset, specifying the target column, selecting a compute type, and setting exit criteria for the job. The presenter also emphasizes the ability to block certain algorithms to save cost and time, and they start an automated ML job while waiting for the results of the previous experiments.
📈 Model Deployment and Consumption
The presenter discusses the deployment of machine learning models as web services or APIs. They explain how to create compute instances for deployment, register the trained model, and deploy it using the inference compute resource. The paragraph also covers the process of consuming the deployed model through provided URLs and sample codes in different programming languages, showcasing the ease of integrating the model into applications.
🌟 Conclusion and Summary of ML Studio Workflow
The final paragraph summarizes the workflow demonstrated in the video. It includes setting up a dataset, building a pipeline with the machine learning designer, running experiments, registering the model, deploying it as an endpoint, and consuming it from applications. The presenter thanks the audience for their attention and highlights the comprehensive nature of the solutions designed in Microsoft Azure Machine Learning Studio.
Mindmap
Keywords
💡Azure Machine Learning Studio
💡Resource Group
💡Workspace
💡Automated ML
💡Designer
💡Dataset
💡Pipeline
💡Compute
💡Model Deployment
💡Endpoints
💡Consume
Highlights
Demonstration of no-code, low-code development using Azure Machine Learning Studio
Process of creating an Azure Machine Learning workspace and selecting relevant services
Explanation of resource group functionality for billing and management purposes
Creation of a resource group named RG001 in the Southeast Asia region
Introduction to Azure Machine Learning as an enterprise version for development purposes
Walkthrough of creating a workspace named WS001 for machine learning operations
Description of resources created under the workspace, including storage account and key vault
Access to Microsoft Azure Machine Learning Studio through a different portal interface
Overview of the main menu in ML Studio, including authoring and automated ML options
Discussion on assets in ML Studio, such as data, pipelines, experiments, models, and endpoints
Tutorial on using the Designer for machine learning without extensive coding
Procedure for data cleaning and feature selection using ML Studio's no-code interface
Guidance on choosing a machine learning model and training it with a dataset
Explanation of how to split data for training and validation purposes
Steps to evaluate the performance of a trained machine learning model
Selection and configuration of compute resources for running ML pipelines
Demonstration of deploying a model as an endpoint for application integration
Comparison of model performance between Designer and Automated ML approaches
Process for consuming and testing the deployed machine learning endpoints
Summary of implementing and designing solutions in Microsoft Azure Machine Learning Studio
