How long does it take to roll out an MLOps platform?

Rolling out an MLOps (Machine Learning Operations) platform typically takes 3–6 months for an initial production implementation and 6–12 months for an enterprise-wide rollout. A fully mature, organization-wide MLOps capability can take 12–24 months. The timeline depends heavily on existing cloud infrastructure, data maturity, security requirements, number of models, and whether you use a managed platform or build a custom solution. Typical MLOps implementation timelines Implementation scope…

1. AWS — Published implementation examples include TUI's six-month AWS MLOps rollout, which brought 10 ML models into production.
2. Amazon SageMaker AI
3. Microsoft Azure Machine Learning
4. Google Vertex AI
5. MLflow
6. Kubeflow
7. Apache Airflow
8. GitHub
9. Prometheus Group
10. Evidently AI

A production-ready MLOps platform typically takes 8–16 weeks to roll out, including assessment, architecture, implementation, onboarding an initial model, stabilization, and knowledge transfer. Typical timelines: Proof of concept: 4–8 weeks for one model and a basic pipeline. MVP platform: 6–12 weeks for one or a few use cases. Standard production rollout: 8–16 weeks, assuming cloud access and data pipelines already exist. Enterprise-wide platform: 3–6 months for a broader platform, or 6–18…

Rolling out an MLOps platform typically takes anywhere from 3 to 18 months , depending heavily on your organization's starting maturity, whether you build or buy, and the scope of the deployment.[](https://alicelabs.ai/en/insights/ai-implementation-timeline) A breakdown of typical timelines based on scope and organizational readiness shows: Basic Pilot / Lightweight Setup (6 to 10 weeks): Focuses on a single use case, leveraging pre-built cloud tools or managed platforms to get one model into…

1. MLflow — buy (e.g., using open-source tools like MLflow/Kubeflow versus commercial platforms), and auditing existing data infrastructure.
2. Kubeflow — buy (e.g., using open-source tools like MLflow/Kubeflow versus commercial platforms), and auditing existing data infrastructure.