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AI Systems Reliability & Security Specialization

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Coursera

AI Systems Reliability & Security Specialization

Build Secure, Scalable Enterprise AI Systems. Design and deploy resilient AI systems with enterprise security and reliability at scale.

Harshita Gulati
Hurix Digital
John Whitworth

Instructors: Harshita Gulati

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Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

4 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Architect resilient multi-cloud AI systems with automated failover, self-healing capabilities, and enterprise-grade security controls.

  • Implement MLOps pipelines with automated experimentation, statistical validation, and ensemble optimization for production deployments.

  • Design zero-trust security architectures with comprehensive governance, compliance automation, and cost optimization strategies.

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Taught in English
Recently updated!

January 2026

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Specialization - 6 course series

What you'll learn

  • Proactive failure analysis builds anti-fragile systems that improve under stress instead of collapsing.

  • Data-driven optimization using RED metrics (Rate, Errors, Duration) drives performance gains and prevents outages.

  • Standardized microservice templates speed development while ensuring operational consistency and security compliance.

  • Resilient architecture comes from defining system boundaries, planning for failures, and implementing full observability.

Skills you'll gain

Category: Service Level
Category: Performance Analysis
Category: Middleware
Category: Performance Metric
Category: Dependency Analysis
Category: Failure Analysis
Category: Performance Tuning
Category: AI Security
Category: Continuous Monitoring
Category: AI Workflows
Category: Distributed Computing
Category: Application Performance Management
Category: Failure Mode And Effects Analysis
Category: Microservices
Category: System Monitoring
Category: Site Reliability Engineering

What you'll learn

  • Model interpretability builds trust by explaining features, identifying bias, and validating AI decisions.

  • Controlled A/B testing turns model changes into evidence by measuring real business impact.

  • Automating experiments helps teams run tests faster, track metrics, and learn consistently.

  • Measuring fairness across demographics helps detect bias and avoid unequal model outcomes.

Skills you'll gain

Category: Test Automation
Category: MLOps (Machine Learning Operations)
Category: Performance Metric
Category: Gap Analysis
Category: Model Evaluation
Category: Test Execution Engine
Category: Cost Benefit Analysis
Category: Content Performance Analysis
Category: Quantitative Research
Category: Quality Assessment
Category: Machine Learning
Category: Data Ethics
Category: Business Metrics
Category: Performance Measurement
Category: Performance Analysis
Category: Feature Engineering
Category: Statistical Hypothesis Testing
Category: Research Design
Category: Key Performance Indicators (KPIs)
Category: Responsible AI

What you'll learn

  • Smart multi-cloud strategy comes from matching workloads to provider strengths through analysis, not vendor habit or preference.

  • Scalable architectures need early bottleneck and resilience planning, since reactive fixes cost far more than proactive design.

  • Effective enterprise architecture requires early, holistic design across security, automation, and operational visibility.

  • Sustainable AI operations rely on architectures that support today’s needs while scaling for future growth.

Skills you'll gain

Category: Multi-Cloud
Category: Cloud Infrastructure
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: CI/CD
Category: Security Controls
Category: Systems Architecture
Category: Data-Driven Decision-Making
Category: Infrastructure As A Service (IaaS)
Category: IT Security Architecture
Category: Enterprise Architecture
Category: Capacity Management
Category: Systems Analysis
Category: Cloud Services
Category: Blueprinting
Category: Scalability
Category: Solution Architecture
Category: Cloud Computing Architecture
Category: Cloud Platforms
Category: Capacity Planning
Category: Cost Containment

What you'll learn

  • Data-driven cloud cost analysis uncovers waste patterns missed by manual checks, enabling targeted optimization and ROI.

  • Effective governance demands continuous evaluation and updates, as policies that worked before may fail as systems scale.

  • Automation shifts governance from reactive fixes to proactive prevention, enabling self-healing, compliant infrastructure.

  • Sustainable cloud operations treat governance policies as living code—versioned, tested, and continuously refined.

Skills you'll gain

Category: Multi-Tenant Cloud Environments
Category: Infrastructure as Code (IaC)
Category: Amazon Web Services
Category: Governance
Category: Scripting
Category: Analysis
Category: Cost Control
Category: Cost Management
Category: Cloud Security
Category: Cloud Management
Category: Automation
Category: Compliance Management
Category: Compliance Auditing
Category: Data-Driven Decision-Making
Category: Terraform

What you'll learn

  • Effective incident response identifies root causes like policy gaps, configuration errors, and design flaws, not just symptoms.

  • Zero-trust architecture shifts security from perimeter-based models to continuous verification for every access request.

  • Security controls must be systematically evaluated against frameworks to spot gaps causing compliance and operational risks.

  • Sustainable data security integrates forensics, proactive architecture, and continuous monitoring into one operations framework.

Skills you'll gain

Category: Failure Analysis
Category: NIST 800-53
Category: Root Cause Analysis
Category: Investigation
Category: Personally Identifiable Information
Category: Cyber Security Assessment

What you'll learn

  • Security monitoring relies on clear behavioral baselines to separate normal admin activity from anomalies that may signal security threats.

  • Infrastructure-as-code enables proactive security governance, preventing vulnerabilities at scale more effectively than reactive incident response.

  • Compliance frameworks support structured risk management and must be continuously reviewed to adapt to evolving security threats.

  • Automated policy enforcement in CI/CD pipelines builds scalable, sustainable security practices that grow with the organization.

Skills you'll gain

Category: Auditing
Category: Threat Detection
Category: Authorization (Computing)
Category: Cyber Security Policies
Category: Encryption
Category: Vulnerability Management
Category: Security Information and Event Management (SIEM)
Category: Cyber Security Assessment
Category: Identity and Access Management
Category: Network Security
Category: Cloud Security
Category: NIST 800-53
Category: Infrastructure as Code (IaC)
Category: DevSecOps
Category: Cloud Computing
Category: Continuous Monitoring
Category: Security Controls
Category: AWS Identity and Access Management (IAM)

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Instructors

Harshita Gulati
Coursera
3 Courses93 learners
Hurix Digital
Coursera
140 Courses3,413 learners
John Whitworth
Coursera
1 Course2 learners

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