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.
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.
Why standard API load-testing assumptions break for LLM inference, and how to design tests that reflect token generation, concurrency, and real serving bottlenecks.
How to define service level objectives for AI systems when correctness is probabilistic, outputs are variable, and traditional uptime metrics miss user-facing failures.
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.
Operational guidance for vector databases in production, including capacity planning, backup strategy, restore testing, and how to think about disaster recovery for embeddings and indexes.
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.
How to measure token-level inference spend in production and add practical controls around prompt size, output limits, routing, caching, and tenant budgets.
How to run ML training workloads on spot or preemptible capacity safely, with checkpointing, interruption handling, retry policy, and pipeline design for fault tolerance.
How to secure AI APIs in production with authentication, tenant isolation, rate limiting, prompt abuse controls, and safer traffic handling around expensive model endpoints.
How to keep PII and sensitive business data out of RAG prompts with pre-retrieval controls, redaction pipelines, access policies, and safer context assembly.
3/30/2026 • 6 min read
3/29/2026 • 8 min read
3/28/2026 • 6 min read
3/27/2026 • 5 min read