MLOpsMachine Learning Operations
The practices and tooling for taking ML and AI systems from notebook to reliable production, covering versioning, deployment, monitoring and evaluation.
· Reviewed by senior engineers
MLOps is to machine learning what DevOps is to software engineering: the practices and tooling for taking models from a notebook to reliable production. It covers data versioning, model versioning, training pipelines, evaluation frameworks, deployment, monitoring for drift, retraining triggers, and the governance layer required by regulated industries.
For classical ML — fraud detection, demand forecasting, churn prediction — MLOps is a mature discipline with established tools (MLflow, Weights & Biases, Kubeflow, Sagemaker, Vertex AI). For LLM-based systems, the equivalent (sometimes called LLMOps) is younger but rapidly converging on a stack: prompt versioning, eval frameworks (Promptfoo, Braintrust, Langfuse), tracing (LangSmith, OpenLLMetry), guardrails, and cost/latency monitoring per request.
The core insight is the same across both: a model is a product, not a project. It needs deployment automation, observability, alerting, rollback, evals running in CI, and a clear answer to "is it still working?" months after the data scientists moved on. The teams that win with AI are the ones that built this discipline; the ones that didn't end up with abandoned proof-of-concepts.
Devinsta brings MLOps and LLMOps practice to every AI build: evals in CI, observability from day one, prompts and models versioned alongside application code, and dashboards finance and product can read.