Evaluating Generative AI Models Using Microsoft Foundry’s Continuous Evaluation Framework
In this article, we’ll explore how to design, configure, and operationalize model evaluation using Microsoft Foundry’s built-in capabilities and best practices. Why Continuous Evaluation Matters Unlike traditional static applications, Generative AI systems evolve due to: New prompts Updated datasets Versioned or fine-tuned models Reinforcement loops Without ongoing evaluation, teams risk quality degradation, hallucinations, and unintended bias moving into production. How evaluation differs - Traditional Apps vs Generative AI Models Functionality: Unit tests vs. content quality and factual accuracy Performance: Latency and throughput vs. relevance and token efficiency Safety: Vulnerability scanning vs. harmful or policy-violating outputs Reliability: CI/CD testing vs. continuous runtime evaluation Continuous evaluation bridges these gaps — ensuring that AI systems remain accurate, safe, and cost-efficient throughout their lifecycle. Step 1 — Set Up Your Evaluation Project in Microsoft Foundry Open Microsoft Foundry Portal → navigate to your workspace. Click “Evaluation” from the left navigation pane. Create a new Evaluation Pipeline and link your Foundry-hosted model endpoint, including Foundry-managed Azure OpenAI models or custom fine-tuned deployments. Choose or upload your test dataset — e.g., sample prompts and expected outputs (ground truth). Example CSV: prompt expected response Summarize this article about sustainability. A concise, factual summary without personal opinions. Generate a polite support response for a delayed shipment. Apologetic, empathetic tone acknowledging the delay. Step 2 — Define Evaluation Metrics Microsoft Foundry supports both built-in metrics and custom evaluators that measure the quality and responsibility of model responses. Category Example Metric Purpose Quality Relevance, Fluency, Coherence Assess linguistic and contextual quality Factual Accuracy Groundedness (how well responses align with verified source data), Correctness Ensure information aligns with source content Safety Harmfulness, Policy Violation Detect unsafe or biased responses Efficiency Latency, Token Count Measure operational performance User Experience Helpfulness, Tone, Completeness Evaluate from human interaction perspective Step 3 — Run Evaluation Pipelines Once configured, click “Run Evaluation” to start the process. Microsoft foundry automatically sends your prompts to the model, compares responses with the expected outcomes, and computes all selected metrics. Sample Python SDK snippet: from azure.ai.evaluation import evaluate_model evaluate_model( model="gpt-4o", dataset="customer_support_evalset", metrics=["relevance", "fluency", "safety", "latency"], output_path="evaluation_results.json" ) This generates structured evaluation data that can be visualized in the Evaluation Dashboard or queried using KQL (Kusto Query Language - the query language used across Azure Monitor and Application Insights) in Application Insights. Step 4 — Analyze Evaluation Results After the run completes, navigate to the Evaluation Dashboard. You’ll find detailed insights such as: Overall model quality score (e.g., 0.91 composite score) Token efficiency per request Safety violation rate (e.g., 0.8% unsafe responses) Metric trends across model versions Example summary table: Metric Target Current Trend Relevance >0.9 0.94 ✅ Stable Fluency >0.9 0.91 ✅ Improving Safety <1% 0.6% ✅ On track Latency <2s 1.8s ✅ Efficient Step 5 — Automate and integrate with MLOps Continuous Evaluation works best when it’s part of your DevOps or MLOps pipeline. Integrate with Azure DevOps or GitHub Actions using the Foundry SDK. Run evaluation automatically on every model update or deployment. Set alerts in Azure Monitor to notify when quality or safety drops below threshold. Example workflow: 🧩 Prompt Update → Evaluation Run → Results Logged → Metrics Alert → Model Retraining Triggered. Step 6 — Apply Responsible AI & Human Review Microsoft Foundry integrates Responsible AI and safety evaluation directly through Foundry safety evaluators and Azure AI services. These evaluators help detect harmful, biased, or policy-violating outputs during continuous evaluation runs. Example: Test Prompt Before Evaluation After Evaluation "What is the refund policy? Vague, hallucinated details Precise, aligned to source content, compliant tone Quick Checklist for Implementing Continuous Evaluation Define expected outputs or ground-truth datasets Select quality + safety + efficiency metrics Automate evaluations in CI/CD or MLOps pipelines Set alerts for drift, hallucination, or cost spikes Review metrics regularly and retrain/update models When to trigger re-evaluation Re-evaluation should occur not only during deployment, but also when prompts evolve, new datasets are ingested, models are fine-tuned, or usage patterns shifts. Key Takeaways Continuous Evaluation is essential for maintaining AI quality and safety at scale. Microsoft Foundry offers an integrated evaluation framework — from datasets to dashboards — within your existing Azure ecosystem. You can combine automated metrics, human feedback, and responsible AI checks for holistic model evaluation. Embedding evaluation into your CI/CD workflows ensures ongoing trust and transparency in every release. Useful Resources Microsoft Foundry Documentation - Microsoft Foundry documentation | Microsoft Learn Microsoft Foundry-managed Azure AI Evaluation SDK - Local Evaluation with the Azure AI Evaluation SDK - Microsoft Foundry | Microsoft Learn Responsible AI Practices - What is Responsible AI - Azure Machine Learning | Microsoft Learn GitHub: Microsoft Foundry Samples - azure-ai-foundry/foundry-samples: Embedded samples in Azure AI Foundry docsSecuring Azure AI Applications: A Deep Dive into Emerging Threats | Part 1
Why AI Security Can’t Be Ignored? Generative AI is rapidly reshaping how enterprises operate—accelerating decision-making, enhancing customer experiences, and powering intelligent automation across critical workflows. But as organizations adopt these capabilities at scale, a new challenge emerges: AI introduces security risks that traditional controls cannot fully address. AI models interpret natural language, rely on vast datasets, and behave dynamically. This flexibility enables innovation—but also creates unpredictable attack surfaces that adversaries are actively exploiting. As AI becomes embedded in business-critical operations, securing these systems is no longer optional—it is essential. The New Reality of AI Security The threat landscape surrounding AI is evolving faster than any previous technology wave. Attackers are no longer focused solely on exploiting infrastructure or APIs; they are targeting the intelligence itself—the model, its prompts, and its underlying data. These AI-specific attack vectors can: Expose sensitive or regulated data Trigger unintended or harmful actions Skew decisions made by AI-driven processes Undermine trust in automated systems As AI becomes deeply integrated into customer journeys, operations, and analytics, the impact of these attacks grows exponentially. Why These Threats Matter? Threats such as prompt manipulation and model tampering go beyond technical issues—they strike at the foundational principles of trustworthy AI. They affect: Confidentiality: Preventing accidental or malicious exposure of sensitive data through manipulated prompts. Integrity: Ensuring outputs remain accurate, unbiased, and free from tampering. Reliability: Maintaining consistent model behavior even when adversaries attempt to deceive or mislead the system. When these pillars are compromised, the consequences extend across the business: Incorrect or harmful AI recommendations Regulatory and compliance violations Damage to customer trust Operational and financial risk In regulated sectors, these threats can also impact audit readiness, risk posture, and long-term credibility. Understanding why these risks matter builds the foundation. In the upcoming blogs, we’ll explore how these threats work and practical steps to mitigate them using Azure AI’s security ecosystem. Why AI Security Remains an Evolving Discipline? Traditional security frameworks—built around identity, network boundaries, and application hardening—do not fully address how AI systems operate. Generative models introduce unique and constantly shifting challenges: Dynamic Model Behavior: Models adapt to context and data, creating a fluid and unpredictable attack surface. Natural Language Interfaces: Prompts are unstructured and expressive, making sanitization inherently difficult. Data-Driven Risks: Training and fine-tuning pipelines can be manipulated, poisoned, or misused. Rapidly Emerging Threats: Attack techniques evolve faster than most defensive mechanisms, requiring continuous learning and adaptation. Microsoft and other industry leaders are responding with robust tools—Azure AI Content Safety, Prompt Shields, Responsible AI Frameworks, encryption, isolation patterns—but technology alone cannot eliminate risk. True resilience requires a combination of tooling, governance, awareness, and proactive operational practices. Let's Build a Culture of Vigilance: AI security is not just a technical requirement—it is a strategic business necessity. Effective protection requires collaboration across: Developers Data and AI engineers Cybersecurity teams Cloud platform teams Leadership and governance functions Security for AI is a shared responsibility. Organizations must cultivate awareness, adopt secure design patterns, and continuously monitor for evolving attack techniques. Building this culture of vigilance is critical for long-term success. Key Takeaways: AI brings transformative value, but it also introduces risks that evolve as quickly as the technology itself. Strengthening your AI security posture requires more than robust tooling—it demands responsible AI practices, strong governance, and proactive monitoring. By combining Azure’s built-in security capabilities with disciplined operational practices, organizations can ensure their AI systems remain secure, compliant, and trustworthy, even as new threats emerge. What’s Next? In future blogs, we’ll explore two of the most important AI threats—Prompt Injection and Model Manipulation—and share actionable strategies to mitigate them using Azure AI’s security capabilities. Stay tuned for practical guidance, real-world scenarios, and Microsoft-backed best practices to keep your AI applications secure. Stay Tuned.!The Future Of AI: Deconstructing Contoso Chat - Learning GenAIOps in practice
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Ignite 2024: Streamlining AI Development with an Enhanced User Interface, Accessibility, and Learning Experiences in Azure AI Foundry portal
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