Janet Xiu Shi
AI Advisory for Trusted Adoption
Quality and Governance
All powered by AWS ecosystem
About
I'm Janet Xiu Shi, a strategic AWS builder and data-first AI systems thinker. I design and implement scalable architectures that turn raw data into trusted intelligence. My focus is on the intersection of AWS infrastructure, data quality, and responsible AI governance.
I work at the strategic level to understand why systems matter, not just how to build them. Data foundations, ML to AI maturity, architectural confidence, and trusted decision-making are the elements that separate ambitious systems from credible ones.
Janet's Framework
A systematic approach to building trustworthy AWS and AI systems. From discovery through delivery, each phase builds intentional architecture and governance.
Discover
Understanding requirements, data landscape, and strategic context. Deep discovery of systems, stakeholders, and constraints.
Design
Architecture design, data modeling, system patterns. Creating blueprints for scalable, governed systems.
Develop
Implementation on AWS, building pipelines, ML systems, and governance controls. Turning design into working code.
Deploy
Launching systems to production with confidence. Testing, security validation, and deployment automation.
Deliver
Measuring outcomes, optimizing performance, and ensuring trusted AI operation. Continuous improvement and governance.
Agent Boutique by Janet™
A team of specialized digital agents designed to extend strategic thinking across research, web building, quality, and marketing.
Janet Xiu Shi
AGENT BOUTIQUE
Maya by Janet Xiu Shi™
AI Strategy & Advisory | Digital Twin
Ming the Quality Expert
AI Evaluation & Governance
Maggie the Connector
Connecting Solutions & People
How I Think
Four pillars of trusted AI: strong data foundations, intelligent systems, responsible governance, and AWS credibility.
Quality data structure, observability, and readiness matter before ML or AI. Strong foundations prevent cascade failures.
Models are part of a wider lifecycle. I think in pipelines, evaluation, and feedback loops, not isolated predictions.
Responsible AI, security thinking, and practical safeguards. Built-in accountability, not retrofitted compliance.
Deep literacy across services, security, scale, and delivery trade-offs. Credible design and implementation on the platform.
From Data Foundations to AI Systems
A curated dashboard-style showcase of how structured data, ML readiness, governance checks, and cloud delivery shape intelligent systems.
Quality improves systematically when tracked and addressed.
Balanced performance across accuracy, precision, and recall.
AI Quality Governance for Trusted Adoption
High-level governance covering AI quality, assurance, guardrails, accountability, and trusted deployment.
Responsible AI Framing
Think beyond model output to impact and accountability. AI systems affect people and businesses; governance ensures we account for that.
Risk & Assurance Awareness
Understand AI failure modes, misuse vectors, and governance needs. Proactive identification of risk is better than reactive mitigation.
Guardrails & Oversight
Design with boundaries, human review, and control in mind. Automation without guardrails is a liability, not a feature.
Trusted AI Maturity
Connect innovation with confidence and operational credibility. Trust is earned through demonstrated care and transparency.
This section signals governance maturity without revealing proprietary approaches. The discipline of thinking about guardrails, oversight, and responsible deployment is what separates production AI from experiments.
Selected AWS and AI Build Stories
Curated examples showing how I combine AWS architecture, data discipline, and AI governance in practice.
Designed and implemented a scalable data pipeline on AWS from raw ingestion to ML-ready datasets. Emphasis on quality gates and observability.
Reduced data latency by 60% through architectural optimization
Built a comprehensive evaluation framework tracking model performance across accuracy, precision, recall, and fairness metrics in production.
Enabled confident deployment decisions through multi-axis evaluation
Implemented guardrails and oversight mechanisms for an AI-powered decision system. Included human review loops and audit trails.
Maintained model accuracy while adding 4 layers of operational governance
Architected a real-time analytics dashboard for AWS infrastructure metrics, enabling proactive scaling and cost optimization.
Reduced compute costs by 35% through data-driven optimization