AI-First Well-Architected Systems: Designing Cloud & AI Architectures- Full Day
In this full-day hands-on workshop, you’ll learn how to design and build systems that are AI-ready, cloud-native, and built to scale. Leveraging the latest from AWS’s Generative AI Lens, Salesforce’s Well-Architected for Data Cloud & Einstein, and other frameworks (Azure, Google Cloud, Databricks), you’ll gain the patterns, tools, and trade-offs to create resilient, efficient, and secure AI + cloud architectures.
Whether you're deploying inference APIs, building agentic systems, or transforming legacy workloads—walk away with design strategies and architecture playbooks you can apply immediately.
Audience Fit:
Cloud Architects → Apply WA pillars to AI workloads
AI/ML Engineers → Build inference & training pipelines with resiliency
DevOps/SRE → Implement observability, failover, scaling, and FinOps
Engineering Leaders → Drive org-wide adoption of AI-ready patterns
Workshop Method:
Level 1: Core WA pillars applied to AI
Level 2: Case studies + trade-off exercises (e.g., GPU cost vs latency, caching vs model freshness)
Hands-on: Group WA review of a sample AI system
Framework Coverage:
AWS: WA Generative AI Lens, Bedrock AgentCore, Prescriptive Guidance on agentic AI
Salesforce: Trusted / Easy / Adaptable + Einstein AI integrations
Google Cloud & Azure: AI/ML perspectives within WA frameworks
Databricks: Well-Architected Lakehouse guidance
Module 1 Foundations & Industry Frameworks - • What makes AI-native workloads different vs traditional cloud-native • Survey of Well-Architected frameworks (AWS, Salesforce, Azure, Google) and their AI/ML perspectives / lenses • Responsible AI, governance, data privacy baseline
Module 2: Reliability, Resilience & Observability • HA patterns for inference APIs and agents • Blast radius control & failure isolation • Recovery strategies (RTO, RPO, model fallback)
Module 3: Performance Efficiency & Scalability • Scaling strategies (vertical, horizontal, serverless triggers) • Bursty inference workloads • DBs, caches, embeddings – Trade-offs: GPU pooling vs autoscaling vs inference acceleration – Caching embeddings for RAG vs recomputation – CDN strategies for generative content – Benchmarking latency vs throughput in real-time agents
Module 4: Cost Optimization & Sustainability • Training vs inference cost trade-offs • Optimizing compute/storage • Spot instances & FinOps – Cost patterns: reserved vs spot vs serverless GPU bursts – Tracking AI spend across vector DBs, model hosting, orchestration layers – Rightsizing GPU/CPU balance – GreenOps: carbon cost modeling for inference workloads
Module 5: Security, Compliance, & Access Control • IAM, network isolation, protecting data, model & prompt security • Regulatory concerns (GDPR, HIPAA, etc.), audit, logging • Threat models specific to AI (e.g. model theft, prompt misuse). Threats: model inversion, data exfiltration, prompt injection • Zero-trust design for AI services
Module 6: Hybrid & Agentic AI, Future Trends • Architecting agentic AI systems • Hybrid & edge deployments • Patterns for evolution: experimentation → production – AWS Bedrock AgentCore, Agentforce, orchestration patterns (Supervisor, Pub/Sub, Router) – Edge inference trade-offs: latency vs cost vs control – Governance for multi-agent systems (auditability, rollback) – Future of WA: AI lifecycle management across frameworks
About Rohit Bhardwaj
Rohit Bhardwaj is a Director of AI & Data Architecture at Salesforce, where he focuses on enterprise AI, agentic systems, cloud-native architecture, distributed systems, data platforms, security, and large-scale transformation.
Over his career, Rohit has designed and led complex enterprise platforms across AWS, Google Cloud, microservices, real-time data, API ecosystems, resilient distributed systems, and AI-enabled architectures. His work increasingly focuses on the challenges enterprises face as software evolves from deterministic services to AI-native and agentic systems—particularly around reliability, governance, evidence, security, observability, cost, and safe autonomy.
Rohit is the author of System Design with AI Interview Guide: Designing Scalable, Agentic, and Defensible Systems, published by Apress. The book presents a modern approach to system design covering scalability, distributed systems, AI architecture primitives, security, reliability, economics, agentic systems, and real-world architectures including e-commerce, ride sharing, payments, fraud detection, messaging, video streaming, file storage, and search. (Springer Link)
Book:
Amazon: https://a.co/d/09Zs1twa
Publisher / Springer Nature: https://link.springer.com/book/10.1007/979-8-8688-2782-2
O'Reilly: https://learning.oreilly.com/library/view/system-design-with/9798868827822/
Rohit is also an O’Reilly instructor and a frequent speaker at technology conferences including No Fluff Just Stuff, UberConf, GIDS, and other international events. His talks focus on practical architecture lessons from building and operating complex systems, including AI control planes, trusted agents, inference at scale, evidence-first RAG, AI security, distributed-system failure, and AI-era software architecture.
As a trusted advisor and architecture leader, Rohit works at the intersection of business strategy and deep technical architecture—helping teams translate complex business problems into scalable, resilient, secure, and economically sustainable systems.
Rohit holds an MBA in Corporate Entrepreneurship from Babson College and graduate-level education in Computer Science from Boston University and Harvard University.
Connect with Rohit:
LinkedIn: http://linkedin.com/in/rohit-bhardwaj-cloud
X / Twitter: @rbhardwaj1