We share everything we learn — real use cases, real production lessons. Technical deep-dives on MLOps, model deployment, AI reliability, and more.
📝 Building in public
Posts authored by the Resilio Tech Team. More in-depth tutorials and case studies coming soon.
A deep guide to production infrastructure for AI agents, covering orchestration patterns, tool-call reliability, retry and timeout strategies, cost controls for agentic loops, and observability for multi-step systems.
A tactical guide to debugging low GPU utilization in ML and inference workloads, covering nvidia-smi profiling, CPU-bound preprocessing, batch-size tuning, async data loading, and model optimization.
A deep guide to high availability AI infrastructure, covering multi-AZ model serving, GPU workload health checks, graceful degradation, fallback models, and circuit breakers for external LLM APIs.
A tactical guide to model rollback in production, covering pre-baked rollback strategies, keeping previous model versions warm, automated rollback triggers, and testing rollback before an incident.
A tactical production readiness checklist for teams deploying their first AI or ML model, covering ownership, data access, model packaging, monitoring, rollback, security, and operational support.
A practical infrastructure guide for running AI workloads in regulated industries, covering SOX, HIPAA, PCI DSS, GDPR, private clusters, air-gapped registries, encryption, access controls, and audit evidence.
A tactical decision framework for choosing between self-hosting AI models and using API providers, covering data sensitivity, latency, cost at scale, customization needs, and team capability.
A tactical guide to evaluating AI infrastructure vendors, including a downloadable RFP template enterprises can use for MLOps and AI platform procurement.
A deep guide to ML microservices architecture, covering when to split feature engineering, training, and serving into separate services, how to define API contracts, and how to avoid a distributed monolith.
A deep guide to media AI infrastructure, covering recommendation serving, automated moderation, and generative AI features at production scale.
A deep guide to model registry best practices, covering MLflow vs custom registries, immutable model versioning, lineage tracking, and promotion workflows from staging to canary to production.
A deep guide for B2B SaaS teams adding AI features like smart search, auto-categorization, and predictive analytics without rebuilding their stack, with guidance on multi-tenant serving, cost isolation, and latency budgets.
4/28/2026 • 16 min read
4/28/2026 • 8 min read
4/28/2026 • 16 min read
4/28/2026 • 7 min read