We rebuilt a failing activation stack as a governed platform using Segment, Databricks, and Iterable, reducing incidents and enabling safer self-service.
Use a query router for LLM analytics — Redshift (KPIs), OpenSearch (definition), Neptune (lineage), and Cache (repeats) — to improve accuracy, latency, and costs.
GPU-as-a-Service makes it easier to share accelerators, but it also raises concerns about isolation and security. This introduces a secure Kubernetes architecture.
Migrating legacy monolithic systems to the cloud is risky. Here is a proven pattern for automating regression testing at scale by replaying production traffic.
Cloud cost is a distributed systems failure mode. This article explains how to make it observable, prevent waste, and manage spend as an operational metric.
This article explains how to build a self-healing observability system with AWS Bedrock AgentCore using AI agents to analyze and remediate infrastructure issues.
MCP is production-ready for LLM-to-tool integration; A2A enables emerging multi-agent collaboration. They complement, not compete, and neither replaces Spark or Airflow.
H100 GPUs are best for flexibility, fast iteration, and custom CUDA work. TPU v5p wins on GCP for large-scale LLM training with better cost efficiency and scaling.
Cloud systems drift when exceptions accumulate, and decisions lose connection to original objectives. Clear requirements and early security design prevent sprawl.
The blog introduces you to the four pillars of observability, AWS and Azure cloud-native services, and ROI to help in architects and engineer's quest for system clarity.