Blog
Insights, guides, and best practices on MLOps, DevOps, Cloud, and AI from the Eprecisio team.
What Mythos 5's Shutdown Means for AI-Native Builders
The first US frontier model to hit hard export controls just took a generation of capability off the table for every customer worldwide. The story is interesting. The engineering lesson is more important.
One Month SaaS Revamp: AI-Assisted Dev, Test-First Policy, Weekly Releases
Three years of production SaaS accumulates debt quietly. Songplace went from monthly releases and fragile staging to weekly shipping in one month using an AI-assisted development loop and a test-first policy.
Production Alerting for an AI Gaming Platform: PagerDuty, Prometheus, Grafana
A live AI gaming platform had solid infrastructure but zero structured alerting. We built a 3-tier PagerDuty, Prometheus, Grafana, and Loki stack in three weeks. Here is what we built and why.
MLOps Tools We Actually Use in Production and Why We Picked Them
Not a listicle. This is the MLOps toolchain we run for production workloads, why we chose each tool over its alternatives, and the honest limitations we tell clients before they commit.
GPU Workload Optimization: What Actually Moves the Needle
GPU utilisation sitting at 30-40% while the model seems slow is almost never a model problem. Here is what we actually find and fix when auditing GPU infrastructure in production.
Scaling ML with Kubernetes: What Production Actually Looks Like
Running ML workloads on Kubernetes looks straightforward until your first multi-GPU training job silently runs 35% slower than it should. Here is what production Kubernetes for ML actually requires.