We share everything we learn — real use cases, real production lessons. Technical deep-dives on MLOps, model deployment, AI reliability, and more.
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Posts authored by the Resilio Tech Team. More in-depth tutorials and case studies coming soon.
Most ML models that work in notebooks break in production. Learn the top reasons for production ML failures — model drift, infrastructure gaps, and monitoring blind spots — and how to fix them.
A practical guide to AI observability for production systems — including latency, drift, token usage, retrieval quality, and the dashboards teams actually use during incidents.
A practical guide to rolling out ML models safely in production using shadow traffic, canary promotion, quality gates, and fast rollback paths.
A hands-on guide to building production MLOps pipelines on Kubernetes — covering CI/CD for models, automated retraining, model registry integration, and deployment strategies.
How to build practical incident response runbooks for production AI systems, including triage flows for latency spikes, drift, bad outputs, and model-serving failures.
How to design an LLM gateway for production use cases, including multi-model routing, guardrails, quotas, usage logging, and cost-aware fallbacks.
A practical guide to batching LLM inference workloads, including static batching, dynamic batching, queue controls, and when higher throughput starts hurting latency.
Why feature freshness failures are so damaging in production ML, and how to detect, contain, and prevent stale features before model quality drifts in silence.
Why prompts need versioning, change control, and rollback paths just like code and model releases, especially when LLM behavior changes under real traffic.
A pragmatic guide to internal ML platforms on Kubernetes, covering the patterns that reduce platform sprawl and the abstractions teams actually use in production.
How to use Terraform to provision AI infrastructure safely, with practical guidance on GPU node pools, registries, pipeline dependencies, and avoiding drift across environments.
A practical guide to handling secrets in AI pipelines, from provider API keys and model registry credentials to access controls around weights, training jobs, and serving systems.
3/30/2026 • 6 min read
3/29/2026 • 8 min read
3/28/2026 • 6 min read
3/27/2026 • 5 min read
