🤖 Agentic AI — Foundations
- Audience: Developers and architects toward an agentic architect mindset. Prerequisites: Python, Notebook; APIs/HTTP; architecture basics.
- Throughline: An agentic layer between UI and microservices—active and autonomous, not passive CRUD.
- Capstone: Diagrams, MCP, workflows, ADLC (evaluate, AgentOps, secure).
📚 Modules
- 01: 🏪 Passive stack → agentic layer: MCP, context, multi-agent patterns.
- 02: 📈 GenAI → agentic Consultant → worker → factory; scope, action, state.
- 03: 🎯 AI agent Perceive–reason–act; agents vs scripts/workflows; cost, safety, observability.
- 04: 🧠 Anatomy LLM, senses, plan, memory, tools, learning, voice; weather & research labs.
- 05: 🔁 PRAL Perception–reason–action–learning; normalization and closing the loop.
- 06: 👥 Multi-agent One-worker limits; orchestration vs choreography; system failures.
- 07: 🔀 Workflows Graphs, routing; handoff, group chat, frameworks.
- 08: 🎨 Patterns Planning, reflection, ReAct, router/specialist, handoff, swarm, HITL, ejection.
- 09: 📚 Agentic RAG Researcher loop vs naive RAG; plan–search–reflect
- 10: 🔌 Protocols ACP, A2A, AG-UI, MCP; adapters; e-shop case.
- 11: 🧩 Context Engineered context vs one prompt; window limits.
- 12: 🏭 ADLC Lifecycle, evaluation, AgentOps, security, MCP tools, four pillars.
- 13: ✅ Closing Agentic architect synthesis; capstone ties patterns, MCP, ADLC.
🏗️ System Design Foundations
Welcome to System Design Foundations. This path builds strong architectural thinking from first principles through modern cloud-native systems. It is grounded in the Well-Architected Framework.
The journey supports early-career engineers building foundational knowledge and experienced practitioners deepening system design across data center infrastructure, cloud platforms, and modern architecture patterns.
What you get
Sixteen (16) progressive modules (about 170 sections) move from core fundamentals to advanced patterns, cloud infrastructure, and the Well-Architected Framework. Every is aligned with the six WAF pillars and mapped to lifecycle phases so you see why a topic matters and when it applies.
Pillars: Operational Excellence ⚙️, Security 🔒, Reliability 🛡️, Performance Efficiency ⚡, Cost Optimization 💰, Sustainability 🌱.
📚 Modules
- 01: 🌐 Foundation & Networking, understand the plumbing of the internet and the physical world underneath it.
- 02: 🔌 API & Communication, learn how services communicate — from RESTful APIs to real-time streaming protocols.
- 03: 🗄️ Data Storage & Databases, dive deep into storage engines, scaling patterns, and cloud-managed database services.
- 04: ⚡ Caching & Performance, master caching from fundamentals through cloud-managed services.
- 05: ⚖️ Load Balancing & Scalability, distribute traffic intelligently and scale systems to meet demand.
- 06: 🧠 Distributed Systems Theory, learn the theoretical foundations that make large-scale distributed systems possible.
- 07: 🛡️ Resilience & Reliability Patterns, build systems that survive failure- from circuit breakers to chaos engineering.
- 08: 📨 Messaging & Event-Driven Architecture, decouple services with asynchronous messaging and event-driven patterns.
- 09: 🏛️ Microservices & Architecture Patterns,d esign, containerize, and orchestrate modern microservices architectures.
- 10: 💾 Storage Systems, go beyond databases into distributed file systems, search engines, and data center storage.
- 11: 🔐 Security, secure systems end-to-end — from identity to infrastructure to compliance.
- 12: 📊 Observability. measure, monitor, and improve systems with modern observability stacks.
- 13: 🧩 Miscellaneous Concepts, round out the toolkit with cross-cutting concerns and operational practices.
- 14: 🔬 Advanced Topics, explore ML systems, recommendation engines, and large-scale data processing.
- 15: ☁️ Cloud Infrastructure & DevOps, build and operate cloud-native platforms end-to-end.
- 16: 🏛️ Well-Architected Framework, tie everything together through the lens of the Well-Architected Framework.
🏗️ Data Design Foundations
Data Design Foundations is a lifecycle-driven course. You learn to design, implement, operate, and evolve data systems across data centers, cloud, and hybrid environments. Every module aligns to the AWS Well-Architected Framework so data design choices are infrastructure-ready from day one. Learn in sequence from Build to Resolve for full coverage, or focus on the phase that matches your project. Pair each module with hands-on artifacts such as a model, migration, dashboard, or runbook.
📚 Modules
- 01: 📘 Data Design Fundamentals, what data design is, principles, lifecycle, requirements analysis, and classification.
- 02: 🧩 Conceptual Data Modeling, entity-relationship modeling, domain modeling, business rules, and data flow modeling.
- 03: 🔗 Logical Data Modeling normalization, denormalization, data types, keys, and relationship patterns.
- 04: ⚙️ Physical Data Modeling, table design, indexes, storage engines, partitioning, and DC storage, compute, and network.
- 05: 🏗️ Relational Database Design Patterns, schema patterns, transactions, views, stored procedures, and constraint-driven design.
- 06: 📄 NoSQL Data Design, document, key-value, wide-column, and graph stores, and choosing the right model.
- 07: 📊 Dimensional Modeling & Analytics Design, star and snowflake schemas, SCD, Data Vault, OLAP, metrics layer, and cloud or on-prem analytics.
- 08: 🔀 Data Integration & Pipeline Design, ETL vs ELT, pipeline and ingestion patterns, transformation, orchestration, and file formats.
- 09: 🔄 Schema Evolution & Data Migration migration strategies, zero-downtime schema changes, versioning, and database refactoring.
- 10: 🏛️ Data Architecture Patterns data mesh, lakehouse, event-driven, microservices data, streaming, polyglot persistence, and hybrid.
- 11: 🚀 Infrastructure Provisioning & Deployment IaC for data, DC provisioning, cloud networking, and CI/CD for data systems.
- 12: ✅ Data Quality & Validation quality dimensions, profiling, validation strategies, cleansing, and quality in pipelines.
- 13: 📜 Data Governance & Compliance governance framework, metadata, lineage, MDM, privacy, compliance, and data security design.
- 14: 🔧 Data Operations & Maintenance DB health monitoring, maintenance, backup and recovery, capacity planning, and pipeline operations.
- 15: ⚡ Performance Tuning & Optimization query and index tuning, connection and resource optimization, caching, and pipeline performance.
- 16: 🔎 Troubleshooting & Problem Resolution diagnosing query and integrity issues, replication, storage, pipeline failures, and incident response.
- 17: ☁️ Cloud Data Services Reference managed relational, NoSQL, warehouse, lake, integration, and governance services.
- 18: 🏛️ Well-Architected Review for Data Systems Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, Sustainability.
🧠 AI & ML Foundations
Structured learning content for building, developing, maintaining, and resolving real-world AI systems — aligned with the Well-Architected Framework and organized by the Build / Develop / Maintain / Resolve lifecycle.
This course covers 26 topic areas across the full AI and ML landscape, from core prerequisites and classical machine learning through agentic AI, multi-agent systems, enterprise governance, and cross-cutting themes. Every topic maps to Build / Develop / Maintain / Resolve so you can prioritize by where you are in a project.
📚 Modules
- 01: 🔧 Core Prerequisites the foundational skills every AI practitioner needs before diving into ML and GenAI
- 02: 📊 Machine Learning Foundations classical ML concepts that remain the backbone of production AI systems
- 03: 🧬 Deep Learning Foundations neural networks from fundamentals to advanced architectures
- 04: 💬 NLP & Language Processing from classical text processing to modern language understanding
- 05: 🔗 Transformers, Encoders, and Embeddings the architecture powering modern AI — from attention mechanisms to vector search
- 06: 🚀 LLM and GenAI Foundations large language models — from pretraining through alignment to production
- 07: 🎯 Context Engineering designing what context a model sees, when, and in what format
- 08: 📖 RAG - Retrieval-Augmented Generation grounding LLM outputs in real, retrievable knowledge
- 09: 🤖 Agents and Agentic AI autonomous AI systems that reason, plan, and take action
- 10: 🌐 Multi-Agent Systems coordinating multiple AI agents for complex collaborative workflows
- 11: 🔌 MCP - Model Context Protocol Technologies standardized protocol for connecting AI models with tools, data, and services
- 12: ✍️ Prompt Engineering Technologies and Practices systematic approaches to crafting, testing, and managing prompts at scale
- 13: 📦 Data-Centric AI & Synthetic Data putting data quality at the center of AI system performance
- 14: ⚙️ MLOps, LLMOps, and LLOps operationalizing AI from training pipelines to production governance
- 15: 📱 TinyML and Edge AI running AI on microcontrollers and edge devices with extreme constraints
- 16: ⚖️ AI Ethics, Safety, and Governance building AI systems that are fair, safe, transparent, and accountable
- 17: 🏗️ AI Infrastructure and Scalability powering AI workloads at scale — from GPUs to multi-cloud
- 18: 🛡️ Reliability, Security, and Quality Engineering making AI systems resilient, secure, and trustworthy in production
- 19: 🧰 AI Frameworks and Tooling Ecosystem navigating the landscape of tools, frameworks, and platforms for AI development
- 20: 🎯 Product and Delivery Skills turning AI capabilities into impactful products and features
- 21: 📈 Agentic AI Metrics quality, safety, and operational metrics purpose-built for agentic systems
- 22: 🎨 AI Design — Human-Centered + System Design designing AI systems that work for humans and at scale
- 23: 🏢 AI Adoption and Organizational Readiness strategies for scaling AI across teams and the enterprise
- 24: 🔒 AI Security — Application, Model, and Data defending AI systems across the full attack surface
- 25: 📜 AI Regulation and Compliance navigating the evolving regulatory landscape for AI systems
- 26: 🔀 Cross-cutting and Emerging Themes observability, document intelligence, AI for code, and continuous evaluation