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Azure AI Content Safety

By Ashwin Venugopal -
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  How Does Azure AI Content Safety Work? Azure AI Content Safety is designed to work with both text and images, as well as content generated by AI. It can identify and moderate inappropriate material. The visual capabilities of Content Safety are driven by Microsoft's Florence foundation model, which has been trained on billions of pairs of text and images. The analysis of text employs natural language processing methods to enhance the understanding of subtlety and context. Azure AI Content Safety supports multiple languages and is capable of recognizing harmful content in both short and long formats. It is currently available in English, German, Spanish, French, Portuguese, Italian, and Chinese. Azure AI Content Safety features include: Safeguarding Text Content Moderate text scans text across four categories: violence, hate speech, sexual content, and self-harm. A severity level from 0 to 6 is returned for each category. This level helps to prioritize what needs immediate attent...
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Use AI Responsibly With Azure AI Content Safety

By Ashwin Venugopal -
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  Introduction Azure AI Content Safety is an all-inclusive technology made to identify and control dangerous content in both AI-generated and user-generated content. Developers may include sophisticated content safety into their apps and services with the aid of Azure AI Content Safety, an AI solution. Developer teams in charge of hosting online debates are facing increasing difficulties in upholding polite and safe online environments. Azure AI Content Safety finds potentially dangerous content and assists businesses in meeting their own quality standards and legal requirements. The need for improving online content safety has four main drivers: Increase in harmful content - There's been a huge growth in user-generated online content, including harmful and inappropriate content. Regulatory pressures - Government pressure to regulate online content. Transparency - Users need transparency in content moderation standards and enforcement. Complex content - Advances in technology are m...
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Using Azure AI Services Containers

By Ashwin Venugopal -
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  Introduction You can deploy a containerized service that encapsulates a specific Azure AI services service API by using the container images for Azure AI services that are available in the Microsoft Container Registry. To deploy and use an Azure AI services container, the following three activities must occur: The container image for the specific Azure AI services API you want to use is downloaded and deployed to a container host, such as a local Docker server, an Azure Container Instance (ACI), or Azure Kubernetes Service (AKS). Client applications submit data to the endpoint provided by the containerized service, and retrieve results just as they would from an Azure AI services cloud resource in Azure. Periodically, usage metrics for the containerized service are sent to an Azure AI services resource in Azure in order to calculate billing for the service. For billing purposes, you must provision an Azure AI services resource in Azure even if you are using a container....
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Deploy Azure AI Services In Containers

By Ashwin Venugopal -
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  Introduction Containers allow you to run Azure AI services either in your local environment or within Azure. For instance, if your application relies on sensitive information stored in an on-premises SQL Server to access an Azure AI service, you can implement Azure AI services in containers on the same network. This ensures that your data remains within your local network instead of being transferred to the cloud. Furthermore, deploying Azure AI services in a local container will reduce latency between the service and your data, which can enhance performance. Understand Containers When you launch a software service, it needs to be placed in an environment that supplies the necessary hardware, operating system, and supporting runtime components required by the service. Azure AI services are offered as a cloud service, where the service software is maintained within an Azure data center that supplies the core runtime services, operating system, and hardware. Additionally,...
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Manage Diagnostic Logging

By Ashwin Venugopal -
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  Introduction Diagnostic logging allows you to collect detailed operational data for an Azure AI services resource, which can be utilized to assess service usage and resolve issues. Create Resources for Diagnostic Log Storage To collect diagnostic logs for an AI services resource, you must establish a destination for the log data. Azure Event Hubs can serve as a destination, allowing you to forward the data to a custom telemetry solution, and it can also connect directly to certain third-party solutions. However, in many instances, you will utilize one (or both) of the following types of resources within your Azure subscription: Azure Log Analytics - a service that enables you to query and visualize log data within the Azure portal. Azure Storage - a cloud-based data store that you can use to store log archives (which can be exported for analysis in other tools as needed). It is essential to generate these resources prior to setting up diagnostic logging for your AI service...
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Create Alerts

By Ashwin Venugopal -
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  Introduction Through the development of alert rules, Microsoft Azure offers resource alerting support. You can set up alerts and notifications for your resources based on events or metric thresholds by using alert rules. When an issue occurs, these alerts will make sure that the right personnel is informed. Alert Rules Choose the Azure AI services resource in the Azure portal, then build a new alert rule under the Alerts tab. You must provide the following in order to define the alert rule: The scope of the alert rule, in other words, the resource you want to monitor. A condition on which the alert is triggered. The specific trigger for the alert is based on a signal type, which can be Activity Log (an entry in the activity log created by an action performed on the resource, such as regenerating its subscription keys) or Metric (a metric threshold such as the number of errors exceeding 10 in an hour). Optional actions, such as sending an email to an administrator notifying t...
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Monitor Azure AI Services

By Ashwin Venugopal -
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  Overview Azure AI services allow you to incorporate artificial intelligence into your applications and services. Monitoring Azure AI Services is essential for tracking usage, identifying trends, and recognizing and resolving issues. Azure AI services offer a cloud-driven platform for integrating artificial intelligence features into your applications. As with any software service, it is important to oversee AI services to manage expenses, recognize utilization patterns, and identify possible problems. Monitor Cost One significant advantage of utilizing cloud services is the ability to achieve cost savings by only covering expenses for the services you actually use. Certain resources within Azure AI services provide a free tier with usage limitations, making it beneficial for development and testing; in addition, there are one or more paid tiers that result in charges based on transactions. The exact billing rate varies depending on the type of resource. Plan costs for AI services...
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