Analyzing and Securing AI System Performance

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Paid Certificate Available
Duration & workload

16 hours 24 minutes
Sessions

On-Demand
Subtitles

English

Found in

Part of

Master Agentic AI: Core Principles & Real-World PC

Overview

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This long course develops skills for operational analytics, secure data practices, and governance essential to building trustworthy, auditable agentic systems. You will aggregate and analyze operational metrics, design A/B experiments and statistical tests to validate agent improvements, and craft clear visualizations and alerting rules for stakeholders. The course covers end-to-end data hygiene: cleaning, schema validation, reproducible notebooks with data versioning, and trade-offs between sample size and noise for experimental design. It also addresses security and governance: securing API endpoints per OWASP ASVS, dependency vulnerability analysis, secret-management trade-offs (on-prem vs managed), and threat modeling (STRIDE). Practical tasks include building DBT models for telemetry, configuring alerts, producing reproducible analytic notebooks, and creating STRIDE diagrams with documented mitigations to reduce operational and supply-chain risk.

Syllabus

Analyze Agent Performance: Build and Test

This module trains data analysts, ML engineers, and developers to optimize AI agents built with frameworks like LangChain and Autogen and learn to prove the effectiveness of the agents. You will transform raw logs into actionable KPIs using SQL and dbt, design and execute A/B tests to compare agent versions, and apply statistical methods like the Chi-square test to validate your results. This course equips you to make objective, evidence-based recommendations for deploying agent enhancements, moving from correlation to causation and ensuring your improvements are statistically significant.

Visualize and Alert AI Performance KPIs

This module is for training data analysts, ML engineers, and product managers to monitor the operational health of AI systems by focusing on cost, latency, and impact. You will master data storytelling, transforming complex performance data into clear, compelling visualizations that drive decisions. Through hands-on labs, you will learn to build proactive monitoring systems by defining critical KPIs, setting precise thresholds, and configuring automated alerts. By the end, you can create dashboards that empower leadership and build automated defenses to protect your AI systems from budget overruns and performance degradation, ensuring real-world success.

Clean, Analyze, and Visualize Your Data

This module, designed for aspiring AI and data professionals, provides hands-on experience in data preparation and exploration. You will learn to build world-class models on high-quality data by implementing systematic cleaning and validation routines with tools like Pandera. In guided Jupyter labs, you will master statistical visualization and dimensionality reduction techniques, such as t-SNE, to transform complex data into clear, interpretable plots. Uncover hidden patterns, diagnose issues, and derive key insights. You'll move beyond just cleaning data to truly understanding it, ensuring your AI development is built on a solid foundation.

Evaluate and Reproduce Data Findings Fast

This module helps data scientists and analysts deliver efficient, trustworthy results. Tackle critical questions like, "Is our data sufficient?" and "Are our findings replicable?" Learn statistical power analysis to optimize sample sizes, preventing wasted resources. You will master reproducible workflows by parameterizing Jupyter notebooks with Papermill and versioning data with DVC. Move beyond simple scripts to build robust, automated analytical projects that accelerate innovation and foster a culture of trust, ensuring your findings can be validated by peers and stakeholders.

Secure AI: API and Dependency Risks

This module transforms developers into defenders, teaching you to build secure, production-grade AI. Learn to harden API endpoints using OWASP guidelines by implementing JWT authentication, input validation, and rate limiting. Adopt an attacker’s mindset, using DAST tools like OWASP ZAP to verify your defenses. You'll master software supply chain security by analyzing vulnerabilities, prioritizing threats with the CVSS framework, and creating hotfix and rollback plans. Through hands-on labs simulating real security incidents, you will be prepared to build and deploy resilient AI services against modern threats.

Secure Your AI: Threat Modeling

This module teaches architects and engineers to build resilience directly into AI system designs. You'll master secret management by comparing self-hosted (Vault) and cloud (AWS Secrets Manager) solutions, using Total Cost of Ownership (TCO) analysis to make a justifiable recommendation. Learn to proactively hunt for vulnerabilities by deconstructing architecture with Data Flow Diagrams and applying the STRIDE framework to mitigate threats. Through hands-on projects, you will draft professional security documents, defend your decisions, and gain the skills to design, build, and maintain secure AI systems from the ground up.

Governance, Alerts and Analytics

In this hands-on module, you'll master governance, alerting, and analytics by building a complete, reproducible telemetry-to-alert pipeline. Using automated notebooks, you will construct a workflow that ingests raw system data and generates critical, real-time alerts. To embed security directly into your design, you will apply the industry-standard STRIDE framework to develop a proactive threat model, identifying and mitigating vulnerabilities before they are exploited. This module will equip you with the skills to translate data into actionable intelligence, creating a robust, automated system for maintaining secure and reliable operations in a production environment.