Knowledge hub

Resources&Insights

Expert guides, code snippets, and actionable playbooks for modern AI infrastructure

7 playbooks, 3 snippets, 5 free tools. Written for DACH/EU production teams.

Production AI

Playbooks explain the theory. The assessment finds your gaps.

Get a fixed-scope stack assessment: heatmap, FinOps register, and 90-day backlog from €4,500.

Book stack assessment

Featured

Featured playbook

FeaturedMLOps20 min read

The Future of MLOps: Trends and Predictions for 2026

Emerging trends in model governance, edge deployment, and platform engineering for production ML teams.

Read playbook

New: interactive learning paths

Structured journeys through platform engineering, MLOps, and LLMOps — glossary concepts, technical guides, and curated open-source resources with trackable milestones.

Start a path

Latest articles

Expert insights and practical guides

6 posts

MLOpsEmail unlock12 min read

The Complete Guide to MLOps: From Theory to Production

Pipelines, registries, evaluation gates, and monitoring patterns for teams shipping models reliably.

Nov 8, 2025Read

LLMOpsEmail unlock15 min read

Building Scalable LLM Applications: Architecture Patterns

Gateways, RAG, caching, and cost controls for LLM systems that survive enterprise traffic.

Nov 5, 2025Read

Cloud10 min read

Cloud Cost Optimization: 10 Strategies That Actually Work

FinOps tactics for GPU workloads, inference routing, and rightsizing without sacrificing reliability.

Nov 1, 2025Read

DevOps8 min read

DevOps Best Practices for High-Performance Teams

CI/CD, release hygiene, and platform patterns that increase deployment frequency with less risk.

Oct 28, 2025Read

DevEx10 min read

Developer Experience: The Secret to Team Productivity

Golden paths, inner-loop tooling, and observability that help AI teams ship weekly instead of quarterly.

Oct 25, 2025Read

CloudEmail unlock14 min read

Kubernetes Security: A Comprehensive Checklist

RBAC, network policies, secrets, and workload hardening for clusters running ML and inference.

Oct 20, 2025Read

Code snippets

Copy-paste production starters

Download the file or copy to clipboard. Each links to the full playbook for context.

HCL

Terraform AWS EKS Module

Production-ready EKS cluster baseline for ML workloads

module "eks" {
  source  = "terraform-aws-modules/eks/aws"
  version = "~> 20.0"

  cluster_name    = var.cluster_name
  cluster_version = "1.31"

  vpc_id     = module.vpc.vpc_id
  subnet_ids = module.vpc.private_subnets

  enable_irsa = true
}

Playbook

YAML

CI/CD GitHub Actions Workflow

Build, scan, and deploy containerized services

name: deploy
on:
  push:
    branches: [main]
jobs:
  release:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: docker/build-push-action@v6
        with:
          push: true
          tags: ${{ env.IMAGE }}:${{ github.sha }}

Playbook

Python

Python MLOps Pipeline

Train, evaluate, and register a model with MLflow

import mlflow

with mlflow.start_run():
    model = train(X_train, y_train)
    metrics = evaluate(model, X_test, y_test)
    mlflow.log_metrics(metrics)
    mlflow.sklearn.log_model(model, "model")

Playbook

Free downloads