A Guide to MLOps (mlops.swiss-ai-center.ch)

A team of software engineers and data scientists at the Swiss AI Center published a practical, hands-on MLOps guide aimed at helping teams move machine learning work from experimentation into production. The guide emphasizes minimizing friction for established workflows—especially in SMEs—by carefully selecting toolchains and providing step‑by‑step instructions for real‑world deployments. It’s designed to be accessible to practitioners at all experience levels and focuses on actionable practices that deliver immediate operational value. For the AI/ML community this matters because it targets the persistent gap between research prototypes and reliable production systems. The guide covers the end‑to‑end model lifecycle: reproducibility and versioning, CI/CD for models, data and model lineage, automated testing, deployment strategies, monitoring and alerting, and scalable data pipelines—while stressing integration with existing DevOps processes and cost/maintenance constraints common in smaller orgs. The pragmatic orientation reduces technical debt and operational surprises, helping teams deliver more robust, auditable, and maintainable ML services faster.