Azure Machine Learning Studio

Dr Tun
14 Aug 202241:32

TLDRThis tutorial demonstrates how to use Azure Machine Learning Studio for no-code and low-code development. It guides through creating a workspace, selecting a resource group, and utilizing Azure's ML capabilities. The process includes data cleaning, feature selection, model training, and evaluation using a designer interface. It also covers automated ML for simplified model creation, the creation of compute clusters for processing power, and the deployment of models as endpoints for application integration. The tutorial concludes with testing the deployed models and consuming the endpoints, showcasing the end-to-end workflow in Azure ML Studio.

Takeaways

  • 🌟 **Azure Machine Learning Studio Overview**: The tutorial covers setting up and using Azure Machine Learning Studio for no-code and low-code development in machine learning.
  • 💻 **Creating a Workspace**: Before starting, you need to create an Azure Machine Learning workspace, which is a central place to manage all your machine learning experiments and deployments.
  • 📈 **Resource Group Management**: A resource group is a logical collection of resources for ease of management and billing, and it's crucial to set this up before creating a workspace.
  • 🔍 **Azure Portal Navigation**: The Azure portal is used to access and manage the services, including searching for and selecting Azure Machine Learning to start the workspace creation process.
  • 🧠 **ML Studio Interface**: ML Studio provides an interface for various machine learning activities, including notebooks, automated ML, and a designer for creating machine learning pipelines without extensive coding.
  • 🔧 **Building a Pipeline**: A pipeline in Azure Machine Learning is a sequence of activities that can include data cleaning, feature selection, model training, and evaluation.
  • 📚 **Data Handling**: The process involves importing data from various sources, such as web files, and using it within the workspace for machine learning experiments.
  • 🛠️ **Model Training and Selection**: The script describes how to train models using datasets and choose algorithms like boosted decision trees for binary classification tasks.
  • 📊 **Model Evaluation and Testing**: After training, models are evaluated for their performance, and endpoints are created for testing and consumption of the machine learning models.
  • ⚙️ **Compute Resources**: Azure Machine Learning allows you to manage compute resources, which are essential for training models and hosting endpoints.
  • 🚀 **Deployment and Consumption**: Once a model is trained and registered, it can be deployed as a web service, and the endpoints can be consumed by applications for making predictions.
  • 📝 **Documentation and Sample Codes**: Azure ML provides documentation and sample codes in various programming languages to help developers integrate the machine learning models into their applications.

Q & A

  • What is the first step in using Azure Machine Learning for no-code or low-code development?

    -The first step is to create an Azure Machine Learning workspace.

  • What is a resource group in Azure and what is its purpose?

    -A resource group is a logical organization of resources for billing purposes and resource management. It allows you to manage and remove resources as a group.

  • What are the three main resources created under a workspace in Azure Machine Learning?

    -The three main resources created under a workspace are a storage account, an application insights instance, and a key vault to store keys.

  • What is the Azure Machine Learning Studio and how does it differ from the classic version?

    -Azure Machine Learning Studio is the enterprise version of Azure Machine Learning. It is designed for no-code or low-code development and includes features like automated ML and a designer interface, differing from the classic version which is more traditional.

  • How does the automated ML feature in Azure Machine Learning Studio work?

    -Automated ML allows users to choose a dataset and label columns, and the system automatically selects the best algorithm to provide a machine learning model without the need for extensive programming.

  • What is the purpose of the 'Designer' feature in Azure Machine Learning Studio?

    -The 'Designer' feature is for data scientists who are familiar with the process of machine learning but prefer not to spend time coding every step. It allows for the creation of machine learning models through a visual interface.

  • What is the role of 'Compute' in Azure Machine Learning?

    -'Compute' in Azure Machine Learning allows users to manage their computing resources, which are necessary for training and deploying machine learning models.

  • How does Azure Machine Learning Studio handle data storage for datasets used in machine learning?

    -Datasets used in machine learning are stored under a data storage account that is created along with the workspace.

  • What is the process for deploying a model as a web service in Azure Machine Learning Studio?

    -After a model is trained and registered, it can be deployed as a web service by specifying a name, selecting a compute resource, and providing necessary files like the score.py file and environment file.

  • How does Azure Machine Learning Studio assist in testing and consuming the deployed endpoints?

    -Azure Machine Learning Studio provides 'Test' and 'Consume' tabs for each deployed endpoint. The 'Test' tab allows users to test the API endpoints, while the 'Consume' tab provides the URL and sample codes in various programming languages for easy integration.

  • What are the key differences between deploying models using the 'Designer' and 'Automated ML' in Azure Machine Learning Studio?

    -Both 'Designer' and 'Automated ML' allow for model deployment as endpoints, but they differ in the approach. 'Designer' involves manually creating a pipeline and selecting components, while 'Automated ML' automates the process of selecting the best model and deploying it.

Outlines

00:00

🌐 Setting Up Azure Machine Learning Workspace

This section guides users on setting up an Azure Machine Learning workspace, starting from scratch with no cloud services running. The user first creates a resource group named RG001 in the Southeast Asia region, followed by a machine learning workspace named WS001. Key steps include selecting the correct subscription, reviewing and creating necessary resources like a storage account, application insights, and a key vault within the created workspace and resource group.

05:07

🛠 Introduction to Machine Learning Tools and Initial Data Setup

The tutorial introduces various machine learning tools within Azure, focusing on no-code options like Automated ML and low-code options like Designer. It details how to start a project by setting up an empty canvas in the Designer tool and importing data using a URL from a GitHub repository containing the Titanic dataset. The video explains the initial steps in data handling, including selecting data types and setting up datasets for further machine learning operations.

10:20

📊 Building and Cleaning Datasets in ML Designer

This section walks through the process of refining the dataset in Azure ML Designer. It covers adding and configuring data cleaning components like removing columns or handling missing data, showing both no-code and low-code methods (using Python or R scripts). The focus is on preparing the dataset for effective machine learning by selecting features manually and ensuring the data is ready for model training.

15:24

🔧 Feature Selection and Preparing the ML Model

Here, the tutorial dives deeper into feature selection, explaining how to choose relevant features for building a machine learning model, like gender and age for the Titanic dataset. The presenter configures a boosted decision tree for binary classification and demonstrates setting up training and validation splits. The section ends with setting up the model training environment.

20:25

🖥 Configuring Compute Resources and Running the ML Model

The presenter explains how to configure compute resources, choosing a cluster over a virtual machine for scalability. A cluster named C001 is created, and the presenter prepares the Titanic designer pipeline for execution by assigning the configured compute resources. Key steps include verifying setup and running the machine learning experiment while discussing potential deployment considerations.

25:30

🔄 Automating Machine Learning and Managing Multiple Experiments

This section introduces Automated Machine Learning (AutoML) in Azure, where the presenter sets up another experiment using a different cluster, C002. The focus is on simplifying machine learning tasks by automating model selection and training processes, optimizing resource usage and cost by blocking unwanted algorithms and setting execution time limits.

30:39

🔍 Monitoring and Deploying Machine Learning Models

The final part of the tutorial discusses monitoring machine learning experiments and deploying models. The presenter registers and deploys a model trained using the Designer as a web service, highlighting how to manage deployment settings, endpoints, and testing APIs. The performance of models from both Designer and AutoML are compared, and the best-performing models are deployed for real-time use.

Mindmap

Keywords

💡Azure Machine Learning Studio

Azure Machine Learning Studio is a cloud-based integrated development environment (IDE) designed for creating machine learning models without the need for extensive coding. It provides tools for data preparation, algorithm selection, training, and deployment of models. In the video, it is used to demonstrate no-code and low-code development for machine learning.

💡Resource Group

A Resource Group in Azure is a logical container that holds related Azure resources for easier management and organization. It is used for billing purposes and can be removed as a group. In the script, the presenter creates a resource group named RG001 for organizing the resources related to the Azure Machine Learning workspace.

💡Workspace

In the context of Azure Machine Learning, a workspace is a centralized place where users can manage various components like datasets, experiments, models, and endpoints. It is created within a subscription and resource group and is fundamental to the machine learning workflow as depicted in the video.

💡Automated ML

Automated ML is a process within Azure Machine Learning Studio that automates the selection of the best machine learning model and algorithm based on the provided dataset. It is designed for users who prefer a more guided approach, reducing the need for extensive programming skills. The video demonstrates how to use Automated ML to quickly generate a machine learning model.

💡Designer

Designer in Azure Machine Learning Studio refers to a visual interface that allows data scientists to build, test, and deploy machine learning models using a drag-and-drop approach. It is aimed at users who are familiar with the machine learning process but prefer not to code every step. The script showcases the use of Designer for creating a machine learning pipeline.

💡Pipeline

A Pipeline in the context of machine learning is a sequence of computational steps applied to data, which can include data cleaning, feature selection, model training, and evaluation. In the video, the presenter uses a pipeline to process the Titanic dataset through various stages of machine learning tasks.

💡Data Cleaning

Data cleaning is the process of preparing and altering raw data to improve its quality and consistency. In the script, the presenter demonstrates a simple data cleaning technique by replacing missing values with the mean, which is a common step in preparing data for machine learning.

💡Feature Selection

Feature selection is the technique of reducing the number of input variables when building a predictive model. It involves selecting the most relevant features, or columns, from the dataset that contribute to predicting the outcome. In the video, the presenter manually selects features such as 'Gender', 'Age', and 'Fare' from the Titanic dataset for the machine learning model.

💡Model Training

Model training is the process of teaching a machine learning model to make predictions or decisions based on example data. The presenter in the video trains a model using a dataset, selecting 'Survived' as the label, and evaluates its performance after training.

💡Compute Cluster

In Azure Machine Learning, a compute cluster refers to a group of computing resources that can be used for training and deploying machine learning models. The presenter chooses to use a cluster for its auto-scaling capabilities and cost efficiency when not in use. The cluster is named C001 in the script.

💡Endpoints

Endpoints in Azure Machine Learning are the entry points for deployed models, which can be consumed by applications as APIs. The video demonstrates the deployment of a model as an endpoint, which allows applications to interact with the model and leverage its predictive capabilities.

Highlights

Azure Machine Learning Studio enables no-code and low-code development for machine learning.

Creating an Azure Machine Learning workspace is the first step in the process.

Resource groups are used for logical organization and management of resources.

Azure Machine Learning includes various services such as storage accounts, application insights, and key vaults.

ML Studio is a portal for conducting machine learning and data science activities.

Automated ML simplifies the process by selecting the best algorithm without extensive programming.

Azure Machine Learning Designer allows data scientists to build machine learning models without extensive coding.

Data scientists can manage datasets, experiments, models, and endpoints within the workspace.

The Titanic dataset is used as an example for demonstrating machine learning concepts.

Data cleaning and feature selection are crucial steps in the machine learning pipeline.

Binary classification models are used to predict outcomes such as survival in the Titanic example.

Splitting data into training and validation sets is essential for training and evaluating machine learning models.

Compute resources in Azure can be managed and scaled according to the needs of the project.

Endpoints are created for deploying machine learning models as APIs or web services.

Automated ML provides an efficient way to select and train the best model for a given dataset.

Endpoints can be tested and consumed with sample codes provided for different programming languages.

Azure Machine Learning Studio streamlines the process of implementing and designing machine learning solutions.

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