Cloud 3.0 & AI Infrastructure Roadmap

Goal: Understand what cloud computing actually is, the different ways it's delivered, and the core building blocks every cloud professional needs to know.

• What is Cloud Computing? On-demand infrastructure explained plainly.
• IaaS, PaaS, SaaS: The three service models and when to use each.
• Deployment Models: Public, private, hybrid, and multi-cloud tradeoffs.
• Regions & Availability Zones: How cloud geography affects reliability and latency.
• Cloud Networking: VPCs, subnets, load balancers, and DNS.

Goal: Master the core services that power every cloud application — virtual machines, containers, storage, and the serverless paradigm.

• VMs & Containers: The difference between EC2, Docker, and why containers won.
• Kubernetes: Orchestrating containers at scale — the industry standard.
• Cloud Storage: Object, block, and file storage — when to use which.
• Serverless: AWS Lambda, Cloud Functions — code without servers.

Goal: Understand the specialized hardware and infrastructure that makes large-scale AI training and inference possible.

• GPU Clusters & Accelerators: A100, H100, TPUs — the engines of modern AI.
• AI Training Infrastructure: How models like GPT-4 are actually trained.
• Distributed Computing for AI: Data parallelism, model parallelism, and tensor parallelism.
• AI-Optimized Networking: InfiniBand and RoCE — why GPUs need special networks.

Goal: Learn the managed AI/ML services from AWS, GCP, and Azure that let you build AI applications without managing infrastructure from scratch.

• Managed ML Platforms: SageMaker, Vertex AI, Azure ML compared.
• AI APIs & Foundation Models: Using GPT, Claude, Gemini via APIs.
• Vector Databases in Cloud: Pinecone, Weaviate, pgvector for RAG applications.
• MLOps on Cloud: Model registries, pipelines, monitoring in production.

Goal: Understand the emerging paradigms defining the next generation of cloud — edge AI, sovereign clouds, federated learning, and sustainable infrastructure.

• What is Cloud 3.0? The shift from data centers to intelligent, distributed infrastructure.
• Edge AI & Federated Learning: AI that runs at the edge and learns without sharing data.
• Sovereign Cloud & AI Governance: Data residency, regulatory compliance, and national clouds.
• FinOps for AI: Managing and optimizing cloud costs for AI workloads.

Goal: Learn how to secure cloud environments and AI workloads, from IAM policies to encryption to zero trust architecture.

• Cloud Security Fundamentals: Shared responsibility model and threat landscape.
• IAM: Users, roles, policies, and least-privilege access control.
• Data Encryption: Encryption at rest and in transit — keys, KMS, and HSMs.
• Zero Trust Architecture: Never trust, always verify — the modern security model.

Goal: Put it all together — design AI-ready cloud systems that are scalable, resilient, cost-efficient, and built for the future.

• AI-Ready Architecture: Designing systems that scale from prototype to production.
• Multi-Cloud & Hybrid AI: Avoiding vendor lock-in and deploying across clouds.
• High Availability & Disaster Recovery: Building systems that never go down.
• The Future of Cloud & AI: What's coming next — neuromorphic chips, quantum cloud, and beyond.