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Accelerate General AI (Gen AI) adoption and optimize AI models and use cases through rapid experimentation. Use Experimentation to iterate quickly on AI models, test different scenarios, and determine effective approaches.
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It helps enhance agility in adapting AI solutions to evolving user needs and market trends, and facilitate understanding of the most effective approaches for scaling AI initiatives.
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### Release defense
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-**CI, CD and continuous experimentation (Gradual feature rollouts and version updates)**
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Ensure seamless transitions and maintain or improve key metrics with each version update while managing feature releases. Utilize experimentation to gradually roll out new features to subsets of users using feature flags, monitor performance metrics, and collect feedback for iterative improvements.
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It's beneficial to reduce the risk of introducing bugs or performance issues to the entire user base. It enables data-driven decision-making during version rollouts and feature flag management, leading to improved product quality and user satisfaction.
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Objective: Ensure smooth transitions and maintain or improve key metrics with each release.
Optimize business metrics by comparing different UI variations and determining the most effective design. Conduct A/B tests using experimentation to test UI elements, measure user interactions, and analyze performance metrics.
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The best return here's improved user experience by implementing UI changes based on empirical evidence.
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Approach: Employ experimentation to gradually roll out new features, monitor performance metrics, and collect feedback for iterative improvements.
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-**Personalization and targeting experiments**
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Deliver personalized content and experiences tailored to user preferences and behaviors. Use experimentation to test personalized content, measure engagement, and iterate on personalization strategies.
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Results are increased user engagement, conversion rates, and customer loyalty through relevant and personalized experiences. These results, in turn drive revenue growth and customer retention by targeting audiences with tailored messages and offers.
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Benefits:
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-**Performance optimization experiments**
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Improve application performance and provide an efficient user experience through performance optimization experiments. Conduct experiments to test performance enhancements, measure key metrics, and implement successful optimizations.
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Here, experimentation enhances application scalability, reliability, and responsiveness through proactive performance improvements. It optimizes resource utilization and infrastructure costs by implementing efficient optimizations.
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* Minimizes the risk of widespread issues by using guardrail metrics to detect and address problems early in the rollout.
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* Helps maintain or improve key performance and user satisfaction metrics by making informed decisions based on real-time data.
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### Test hypotheses
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Objective: Validate assumptions and hypotheses to make informed decisions about product features, user behaviors, or business strategies.
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Approach: Use experimentation to test specific hypotheses by creating different feature versions or scenarios, then analyze user interactions and performance metrics to determine outcomes.
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Benefits:
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* Provides evidence-based insights that reduce uncertainty and guide strategic decision-making.
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* Enables faster iteration and innovation by confirming or refuting hypotheses with real user data.
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* Enhances product development by focusing efforts on ideas that are proven to work, ultimately leading to more successful and user-aligned features.
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### A/B testing
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Objective: Optimize business metrics by comparing different UI variations and determining the most effective design.
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Approach: Conduct A/B tests using experimentation to test UI elements, measure user interactions, and analyze performance metrics.
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Benefits:
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* Improves user experience by implementing UI changes based on empirical evidence.
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* Increases conversion rates, engagement levels, and overall effectiveness of digital products or services.
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### For intelligent applications (for example, AI-based features)
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Objective: Accelerate General AI (Gen AI) adoption and optimize AI models and use cases through rapid experimentation.
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Approach: Use experimentation to iterate quickly on AI models, test different scenarios, and determine effective approaches.
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Benefits:
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* Enhances agility in adapting AI solutions to evolving user needs and market trends.
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* Facilitates understanding of the most effective approaches for scaling AI initiatives.
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* Improves accuracy and performance of AI models based on real-world data and feedback.
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### Personalization and targeting experiments
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Objective: Deliver personalized content and experiences tailored to user preferences and behaviors.
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Approach: Leverage experimentation to test personalized content, measure engagement, and iterate on personalization strategies.
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Benefits:
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* Increases user engagement, conversion rates, and customer loyalty through relevant and personalized experiences.
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* Drives revenue growth and customer retention by targeting audiences with tailored messages and offers.
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### Performance optimization experiments
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Objective: Improve application performance and user experience through performance optimization experiments.
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Approach: Conduct experiments to test performance enhancements, measure key metrics, and implement successful optimizations.
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Benefits:
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* Enhances application scalability, reliability, and responsiveness through proactive performance improvements.
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* Optimizes resource utilization and infrastructure costs by implementing efficient optimizations.
In this guide, you learn how to create reports from your dashboards in Azure Managed Grafana. You can configure to have these reports emailed to intended recipients on a regular schedule or on-demand.
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Generating reports in the PDF format requires Grafana's image rendering capability, which captures dashboard panels as PNG images. Azure Managed Grafana installs the image renderer for your instance automatically.
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> [!IMPORTANT]
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> Reporting and image rendering are currently in preview. See the [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/) for legal terms that apply to Azure features that are in beta, preview, or otherwise not yet released into general availability.
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## Image rendering performance
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Image rendering is a CPU-intensive operation. An Azure Managed Grafana instance needs about 10 seconds to render one panel, assuming data query is completed in less than 1 second. The Grafana software allows a maximum of 200 seconds to generate an entire report. Dashboards should contain no more than 20 panels each if they're used in PDF reports. You may have to reduce the panel number further if you plan to include other artifacts (for example, CSV) in the reports.
>Today, Microsoft Purview doesn't support automated disaster recovery. Until that support is added, you're responsible to take care of backup and restore activities. You can manually create a secondary Microsoft Purview account as a warm standby instance in another region.
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>Today, Microsoft Purview doesn't support automated disaster recovery. Until that support is added, you're responsible to take care of backup and restore activities. You can manually create a secondary Microsoft Purview account as a warm standby instance in another region. Note that this standby instance in another region would not support Microsoft Purview Data Governance Solution. Today, it only supports Azure Purview solution. We are working on adding DR support for Microsoft Purview Data Governance Solution.
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To implement disaster recovery for Microsoft Purview, see the [Microsoft Purview disaster recovery documentation.](/purview/disaster-recovery)
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