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//Cloud-Native Backend & MLOps

Microservices, orchestration, and ML lifecycle infrastructure.

High-performance backends and the MLOps to deploy, monitor, and retrain models in production — for SaaS, AI products, and real-time data platforms.

01What's broken without us

Your backend can't keep up, or your models work in a notebook but never make it to production reliably. There's no path from experiment to deployed and monitored.

02Our approach — the Forge Method, applied
  • 01Architect services and the ML lifecycle around real load and SLAs.
  • 02Prototype deployment and monitoring before scaling.
  • 03Forge microservices, pipelines, and IaC.
  • 04Harden with A/B deployment, drift monitoring, and retraining.
//Capabilities
  • Microservices (Go, Rust, Node.js, .NET)
  • Serverless & container orchestration (K8s, Nomad)
  • CI/CD (GitHub Actions, GitLab CI, ArgoCD)
  • MLOps: deployment, A/B, monitoring, drift
  • Data engineering (feature stores, ETL)
  • Infrastructure as Code (Terraform, Pulumi)
  • Real-time event streaming
  • Event-driven architecture
//Tech stack
  • Go
  • Rust
  • Kubernetes
  • Kafka
  • MLflow
  • Terraform
  • ArgoCD
//Outcomes

What you can expect

Notebook → prod
real ML pipeline
Monitored
for drift
Scales
with load
//FAQ

Questions, answered straight

For high-throughput, low-latency services they deliver performance and safety. We use the right language per service, not one everywhere.

//Explore further
//Next step

Lock your build window.
Ship cloud-native backend & mlops that works.

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