MLOps Maturity Roadmap

The most common failure pattern in MLOps adoption is the platform-first approach: a team decides they need MLOps, evaluates platforms like Kubeflow, MLflow, and SageMaker, spends three months building infrastructure, and then discovers that no one on the team can actually use it because the operational processes, data pipelines, and team skills have not matured alongside the tooling. The platform sits mostly unused while data scientists continue working in notebooks.

The maturity model approach inverts this. Instead of building infrastructure and hoping teams grow into it, you assess your current maturity level, address the specific gaps blocking the next level, and advance incrementally. Each level builds on the previous one, and the team's skills, processes, and tooling evolve together. The result is that every investment in tooling is immediately useful because the team is ready to use it.

At Level 0, data scientists work in notebooks with no standardized workflow. Experiments are tracked in spreadsheets or not at all. Models are deployed by copying files to production servers. Training data lives on individual laptops or in shared folders with no version control. When a model needs to be retrained, someone remembers (or does not remember) which notebook produced the current production model and runs it again with whatever data is available. Most organizations start here, and some stay here far too long.