Streamline Your Azure Labeling Process with Custom Vision

Custom Vision is a powerful tool within Azure that can significantly streamline your labeling process. It allows you to create custom machine learning models that can accurately identify objects in images.

With Custom Vision, you can create projects in minutes and start labeling images right away. This is particularly useful for businesses with large datasets that need to be labeled quickly.

Custom Vision also offers a range of features that make labeling more efficient, including the ability to create custom tags and annotate images with precision.

Azure Label offers a range of benefits and features that can help you protect and manage your organization's sensitive data.

With Azure Information Protection, you get a more in-depth understanding of where your content is being distributed and how it is being utilized, giving you increased visibility of your organizational content.

Azure Information Protection provides an additional layer of protection, which is most useful within the Microsoft 365 ecosystem, including Microsoft Teams and SharePoint.

You can categorize and identify your data according to its sensitivity and relevance using the Azure Information Protection client, which provides the capabilities for classifying, tagging, and securing your documents and communications.

To use Azure Information Protection, you need to follow these steps: Purchase an Azure subscriptionInstall the Azure Information Protection clientClassify and label your dataApply protection policiesMonitor and audit usageCollaborate securely

Azure Information Protection can be used in various ways, including protecting sensitive data by categorizing and marking it according to its sensitivity level, and ensuring compliance with industry requirements and data protection legislation by appropriately labeling and securing sensitive data.

Azure label is a powerful tool that enables businesses to classify and protect sensitive data. The primary components of Azure Information Protection are the AIP universal labeling client, AIP labeling and classification, AIP policies, AIP analytics, and AIP integration.

The AIP universal labeling client is a software program that can be installed on devices to assist users in classifying, labeling, and protecting sensitive data. It connects with several Microsoft products, including Office 365, to make it easy for users to add data protection labels and controls.

AIP policies allow businesses to specify the terms and circumstances under which data protection labels and restrictions will be automatically applied. This can be based on variables like file type or user identification.

AIP analytics provides reporting and analytics tools that help businesses keep track of how their data is used throughout the company. This promotes regulation compliance and aids in the identification of potential security risks.

Here are the five key components of Azure Information Protection:

The MIP SDK extends sensitivity labeling to third-party applications and services, allowing developers to add sensitive labeling functionality to various apps and services. This ensures they are recognized appropriately and can benefit from effective data governance.

The key components of Azure Information Protection are what make it a powerful tool for data protection. AIP universal labeling client is a software program that can be installed on devices to help users classify, label, and protect sensitive data.

The AIP universal labeling client connects with several Microsoft products, including the top productivity apps in Office 365, making it easy for users to add data protection labels and controls. This integration allows users to work seamlessly with protected data.

AIP labeling and classification is a flexible and adaptable method for labeling and classifying items, enabling businesses to set their own standards. This allows users to apply the proper protection rules based on classifications such as confidential, personal, or public.

AIP policies are used to specify the terms and circumstances under which data protection labels and restrictions will be automatically applied. These policies can be altered to satisfy specific company demands and legal constraints.

AIP analytics provides reporting and analytics tools that let businesses keep track of how their data is used throughout the company. This promotes regulatory compliance and helps identify potential security risks.

The following are the primary components of Azure Information Protection:

Custom Vision is a powerful tool for labeling computer vision data in Azure. You can start using Custom Vision once your instance has deployed.

To get started, you'll need to create a Custom Vision instance on your Azure account. This involves searching for "Custom Vision" in the Azure dashboard and clicking the "Create custom vision" button. You'll then be asked to fill out information about your project, including selecting whether you want to train, deploy, or both train and deploy with Custom Vision.

You'll also need to attach your Custom Vision instance to a resource group, select a project name, and choose the pricing tier for your instance. The deployment step can take a few moments, so be patient.

Once your instance is set up, you can create a new project in Custom Vision. This involves opening the Custom Vision dashboard, clicking the "Create Project" button, and providing a name for your project. You'll also need to choose an instance of Custom Vision with which your project should be associated and select a project type (object detection or classification).

For object detection, you'll want to select the YOLOv8 export format and download the zip file to your computer. This will allow you to train an object detection model using Custom Vision.

Here are the steps to create a Custom Vision instance, summarized:

Azure label provides robust protection for various types of assets. You can safeguard documents, emails, images, audio and video files, and data stored in cloud services.

Azure label can protect documents, including text files, PDFs, and Microsoft Office documents. Users can label these files with data to indicate their level of sensitivity, and Azure label can implement security measures based on the labeling regulations.

Azure label can also secure emails by allowing users to apply data labels, and implementing protective rules based on the labeling policies. This ensures that sensitive information is only accessible to authorized personnel.

The types of assets that can be protected with Azure label include:

Having a robust compliance system in place is essential for businesses, and Azure Information Protection (AIP) helps organizations achieve this. AIP complies with industry and governmental standards, including GDPR, HIPAA, and PCI-DSS.

Organizations can classify and safeguard sensitive data in a way that complies with these criteria and upholds data privacy. AIP enables organizations to develop and implement policies automatically, applying data protection labels and control.

This automation reduces the danger of human mistakes and data breaches. By using AIP, organizations can ensure compliance with regulations and maintain the trust of their customers and stakeholders.

Azure Information Protection (AIP) can safeguard a wide range of digital assets, including documents, emails, images, audio and video files, and data stored in cloud services.

Documents are a primary concern for many organizations, and AIP can protect various file types, such as text files, PDFs, and Microsoft Office documents.

Emails are another critical asset that can be protected by AIP, allowing users to apply data labels to indicate their level of sensitivity and implement protective rules based on labeling policies.

Images and audio/video files can also be secured using AIP, either by adding watermarks or limiting user access.

AIP can safeguard data in several cloud services, including Microsoft Teams, OneDrive, and SharePoint, by implementing protective mechanisms based on labeling policies.

Here are some examples of assets that can be protected with AIP:

Azure Information Protection integrates seamlessly with Office 365 to automatically categorize and secure sensitive information in emails and documents.

This integration enables automatic and manual labeling, safeguarding, tracking, and revocation of restricted content. Organizations can ensure uniform and efficient data protection throughout their email and document operations.

By enforcing data protection regulations, Azure Information Protection with Office 365 enables secure collaboration with outside users.

Setting up a project in Azure Label involves creating a Custom Vision project, which has its own web interface separate from the Azure dashboard. To do this, you'll need to click the "Create Project" button and provide a name for your project, choose an instance of Custom Vision, and select a project type (object detection or classification).

To start labeling, you'll need to add new labels to your project. This involves selecting the project, pausing labeling activity, and modifying your labels. You can add new labels by selecting the Details tab, Label categories, and then modifying your labels in the form. You'll also need to choose how to treat data that's already labeled and modify your instructions page as necessary.

Azure Label also allows you to create a text labeling project, which is administered in Azure Machine Learning. To create a text labeling project, select Add project and enter a name for the project. You'll also need to select Text as the Media type and choose a Labeling task type.

Project setup and management can be a daunting task, but with the right tools, you can streamline your processes and make it more efficient. Azure Information Protection offers a simplified data management method that helps organizations manage sensitive data across numerous platforms and devices.

This means you can easily identify, label, and secure sensitive data without much hassle. Azure Information Protection's integration with Microsoft programs like Office 365, SharePoint, and Exchange makes it possible to add labels and protection to documents and emails with ease.

With AIP, you can design rules that automatically apply data protection labels and controls, reducing the need for manual involvement. This feature alone can save you a significant amount of time and effort in the long run.

To create a Custom Vision instance, go to the Azure dashboard and search for "Custom Vision" in the search bar.

You'll be taken to the Custom Vision homepage, where you'll need to click the "Create custom vision" button to begin the process.

Next, you'll be asked to select whether you want to train, deploy, or both train and deploy with Custom Vision, so choose the option relevant to your project.

You'll need to attach your Custom Vision instance to a resource group, so select one from your existing groups or create a new one.

Select a project name that accurately reflects your project's goals and objectives.

Choose the pricing tier for your instance, and be aware that you can learn more about Azure Custom Vision pricing if you need more information.

Once you've filled out the form, click "Review + create" to create a new Custom Vision instance.

The deployment step can take a few moments, so be patient and wait for the status to report in.

Creating a project is a crucial step in setting up your project. You can create a Custom Vision project by clicking the "Create Project" button on the Custom Vision dashboard.

To create a Custom Vision project, you'll need to provide a name for your project, choose an instance of Custom Vision, and select a project type. For this guide, we will train an object detection model.

You'll need to choose between object detection and classification, and click "Create project" when you have configured your project. Select the YOLOv8 export format and select "download zip to computer".

If you're creating a text labeling project, you'll need to initialize the project on the Azure Machine Learning Data Labeling page. You can't change the task type or dataset once the project is initialized, so carefully review the settings before creating the project.

Here's a step-by-step guide to creating a text labeling project:

Make sure to select the correct vendor labeling company, if applicable, and sign a contract before proceeding.

You can add new labels to a project to classify items more accurately. This includes adding an Unknown or Other label to indicate confusion.

To add new labels, select the project on the main Data Labeling page, pause the labeling activity, and then modify your labels on the Details tab.

Consensus labeling is a quality control feature that sends each item to multiple labelers to get more accurate labels. This feature is currently in public preview.

To use consensus labeling, select Enable consensus labeling (preview) and set values for Minimum labelers and Maximum labelers. Make sure you have as many labelers available as your maximum number.

Classification is a crucial step in labeling and quality control. It involves assigning a level of secrecy or sensitivity to sensitive data, which can then be used to enforce access, sharing, and retention policies.

Sensitive data can be found by scanning on-premises and other cloud services with the Azure Information Protection Scanner, which includes documents, emails, photos, and other files. This data can then be categorized and labeled with metadata that specifies its level of secrecy or sensitivity.

The Azure Information Protection Scanner assigns data classification labels to newly discovered data by a set of specified policies. This classification helps identify and secure sensitive data.

A comprehensive labeling and classification system offered by Azure Information Protection allows businesses to tag their sensitive data with metadata that specifies the level of secrecy or sensitivity. Labels can be used to enforce access, sharing, and retention policies and applied to files, emails, and other types of content.

Here are the features of the Azure Information Protection Scanner:

By classifying sensitive data, businesses can increase visibility and control over their sensitive data while guaranteeing compliance with industry standards and data protection requirements.

Adding labels to a project is a crucial step in the data labeling process. You can add new labels to a project by selecting the project on the main Data Labeling page, pausing the labeling activity, and then modifying the label categories.

To create a flat list of labels, select Add label category to create each label. This will allow you to choose among classes and increase accuracy and speed. For example, instead of spelling out the full genus and species for plants or animals, you can use a field code or abbreviate the genus.

To create labels in different groups, select Add label category to create the top-level labels, and then select the plus sign (+) under each top-level label to create the next level of labels for that category. You can create up to six levels for any grouping.

You can select labels at any level during the tagging process. For instance, the labels Animal, Animal/Cat, Animal/Dog, Color, Color/Black, Color/White, and Color/Silver are all available choices for a label. In a multi-label project, there's no requirement to pick one of each category.

If you have an Azure MLTable data asset or COCO file that contains labels for your current data, you can import these labels into your project. To import labels, select the Import button on the project command bar. You can import labeled data for Machine Learning experimentation at any time.

Here's a step-by-step guide to importing labels:

Consensus labeling is currently in public preview, but it's not recommended for production workloads. The preview version is provided without a service level agreement.

To get more accurate labels, use the Quality control page to send each item to multiple labelers. You can specify how many labelers to use by setting values for Minimum labelers and Maximum labelers.

Make sure you have as many labelers available as your maximum number, because you can't change these settings after the project has started. This ensures that you're getting the most out of consensus labeling.

If a consensus is reached from the minimum number of labelers, the item is labeled. But if a consensus isn't reached, the item is sent to more labelers.

You can export labels from your Azure Label project at any time for Machine Learning experimentation. Exporting labels is a straightforward process that allows you to save your labeled data for further use.

For projects other than Text Named Entity Recognition, you can export labels as a CSV file, an Azure Machine Learning dataset, or an Azure MLTable data asset. The exported CSV file is created in a folder inside Labeling/export/csv.

Azure Machine Learning also creates a COCO format file in a folder inside Labeling/export/coco, or an Azure MLTable data asset, depending on your project type. You can download the exported file by selecting the Download file link in the notification that appears when the file is ready.

Here are the export options for different project types:

After exporting your labels, you can access the exported Azure Machine Learning datasets and data assets in the Data section of Machine Learning.

You can import labels from a COCO file or an Azure MLTable data asset into your project, which is super helpful if you've already labeled data in a previous project using the same data. This feature is only available for image projects.

To import labels, select the Import button on the project command bar. You can import labeled data at any time for Machine Learning experimentation.

There are two options for importing labels: As prelabeled data or As final labels. If you choose As prelabeled data, your labeler can review the prelabeled data and correct any errors before submitting the labels.

Exporting your labeled data is a crucial step in machine learning experimentation. You can export the labels at any time by selecting the Export button on the project command bar.

Azure Machine Learning creates the exported file in a specific folder, depending on the project type. For example, a CSV file is created in a folder inside Labeling/export/csv.

You can export label data as a CSV file, a COCO format file, or an Azure Machine Learning dataset with labels. A notification appears briefly when the file is ready to download, and you can select the Download file link to download your results.

For Text Named Entity Recognition projects, you can export label data as an Azure Machine Learning dataset (v1) with labels or a CoNLL file. You'll also have to assign a compute resource for the CoNLL file export, which runs offline and generates the file as part of an experiment run.

Here are the export options for labeled data:

You can also find the notification in the Notification section on the top bar.