Long-form essays from the engineers shipping AI inside payers, hospitals, energy operators and proptech platforms. Written for technology leaders who care more about what runs in production than what trended last week.
Learn Data Lakehouse Architecture Concepts in 2026: open table formats, the medallion model, governance, performance, and the controls every platform needs.
Learn Agentic AI Workflows Concepts in 2026: task decomposition, orchestration, human handoffs, evaluation, and the controls every production workflow needs.
Learn Monolith to Microservices Concepts in 2026: seams, the strangler pattern, data decomposition, contracts, and the controls every migration needs.
Learn ELT Modernization Concepts in 2026: extract-load-transform, the warehouse as compute, orchestration, testing, and the controls every pipeline needs.
Learn GPU Cost Optimization Concepts in 2026: utilization, right-sizing, scheduling, inference efficiency, and the controls every GPU program needs.
Learn Scalable Cloud Architecture Concepts in 2026: statelessness, data partitioning, autoscaling, resilience, and the controls every production system needs.
Learn FinOps Concepts in 2026: cost visibility, allocation and showback, unit economics, optimization, and the controls every cloud spend program needs.
Learn LLMOps Concepts in 2026: prompt and model versioning, evaluation, observability, cost control, and the controls every production LLM system needs.
Learn Enterprise LLM Integration Concepts in 2026: retrieval, grounding, evaluation, cost control, and the guardrails every production LLM feature needs.
Learn Data Reliability Engineering Concepts in 2026: SLAs for data, freshness and quality checks, incident response, and the controls every production pipeline needs.
How to secure the AI supply chain, models, weights, and dependencies, with provenance, verification, and the controls that prevent compromised AI components.
How to design multi-region active-active without the headaches: data consistency, conflict handling, and routing, so the resilience is real and the complexity managed.
How to set data SLAs you can deliver: promising freshness grounded in pipeline reality, measured and met, so data consumers can trust the commitments.
How to set autoscaling policies for spiky AI traffic that balance capacity and cost: scaling fast enough for spikes without paying for peak continuously.
Why the observability bill can exceed compute, and how to control it: sampling, retention, and cardinality discipline that keep telemetry valuable and affordable.
How to set cost guardrails for AI that prevent bill shock: budgets, alerts, and hard limits on inference spend, so a runaway cost is caught before the invoice.
How to pick a first enterprise AI use case that won't embarrass you: choosing for a clear win, bounded risk, and measurable value, so the program earns its next step.
How to manage feature flag debt after progressive delivery: retiring stale flags, so the codebase stays clean and flags do not become complexity and risk.
How to do multi-account AWS showback without spreadsheets: automated cost attribution by account and team, so cost accountability is continuous rather than a manual chore.
How to have the SRE error budget conversation with product: using the budget as a shared decision tool for reliability versus velocity, not a source of conflict.
How to optimize AI inference cost and latency with quantization, batching, and caching, applied to fit each workload's latency and quality constraints.
How data governance must evolve for the AI era: governing data use for training and inference, not just access, with policies that keep pace with how AI uses data.
Why disaster recovery testing is the drill most teams skip, and how regular DR drills turn an untested plan into a recovery capability you can trust.
How to decide whether to build, buy, or wait on an internal AI assistant: weighing differentiation, cost, and a fast-moving market, for enterprise leaders.
One long-form essay every other Wednesday. Written by the engineers shipping production AI for our clients, not by a content team. No promotional emails. Unsubscribe in one click.
