AI Spark: AI-Assisted Infrastructure Capacity Planning
Use AI to analyze usage trends and predict when infrastructure capacity needs to be expanded, avoiding both outages and over-provisioning.
Cloud
Amazon Bedrock vs Google Vertex AI - Cloud AI Platforms Compared
Comparing Amazon Bedrock and Google Vertex AI for foundation model access, fine-tuning, RAG, and enterprise AI deployment.
Cloud Governance
The framework of policies, processes, and controls that organizations use to manage cloud resources, ensure compliance, control costs, and …
Cost Estimation for AWS AI Services
How to estimate and manage costs for AI workloads on AWS, covering Bedrock, SageMaker, compute, storage, and strategies for cost …
Cross-Border Data Transfers for AI
Guide to managing international data transfers for AI systems under GDPR, covering transfer mechanisms, cloud considerations, and practical …
Data Sovereignty
The principle that data is subject to the laws and governance of the country or region where it is collected or stored, critical for AI …
On-Premise vs Cloud for AI Workloads
Comparing on-premise and cloud deployment for AI and ML workloads, covering cost, performance, security, scalability, and decision criteria.
Scaling AI Infrastructure
How to scale AI infrastructure for growing workloads, covering compute scaling, model serving at scale, data infrastructure, and cost …
Hybrid Cloud
What hybrid cloud is, why it matters for AI workloads with data gravity and compliance constraints, and AWS hybrid options including FSx for …
The Well-Architected Framework - Why Every Cloud Provider Has One
What the Well-Architected Framework is, its origins at AWS, how Azure and GCP adopted it, its six pillars, and why it matters especially for …
AI Architecture Patterns - From Monolith to Multi-Agent
How AI system architecture evolves from monolithic single-model deployments through microservices to collaborative multi-agent systems, with …
AI Deployment Models - SaaS, PaaS, IaaS, and Serverless
How the four cloud deployment models apply to AI workloads: when to use managed models, platform endpoints, GPU instances, or serverless …
AWS AI Services vs Azure AI - Complete Comparison
A service-by-service map of AWS AI and ML services to their Azure AI equivalents, covering language models, speech, vision, and MLOps.
AWS AI Services vs Google Cloud AI - Complete Comparison
A service-by-service map of AWS AI and ML services to their Google Cloud equivalents, covering language models, speech, vision, and MLOps.
Serverless Computing
What serverless computing means, how Lambda, Fargate, and Step Functions fit AI workloads, and when serverless is and is not the right …
Terraform - Infrastructure as Code for AI Projects
Using Terraform to provision and manage AWS infrastructure for AI projects: modular design, state management, and multi-environment …