[ MLOPS ]
MLOps: Production Discipline for Machine Learning
MLOps is not a tool stack. It is an operating model. Model registries, feature stores, drift detection, retraining pipelines, evaluation harnesses, on-call rotations. The tools change every two years; the discipline does not. We have run this discipline inside enterprise ML platforms for fifteen years.
[ THE LEVENT POINT OF VIEW ]
MLOps is an operating model, not a vendor selection.
Most "MLOps engagements" are vendor decisions in disguise. Pick Vertex AI, Dataiku, Databricks, SageMaker, choose. The harder problem is the operating model that survives whichever tool you picked: who owns the model in production, how does drift get triaged, what triggers a retrain, what makes a deploy reversible. That is where we spend our time.
[ WHAT THIS MEANS IN PRACTICE ]
We operate the enterprise Dataiku platform for a national energy company across 15+ business entities under a Managed Service contract. Model registries, feature stores, retraining pipelines, drift monitoring, and the L1/L2/L3 incident response posture all run inside our Managed Service model. The discipline ports cleanly to Vertex AI, Azure ML, and other platforms because the operating model is the constant.
The MLOps to AgentOps continuum is not metaphorical. The same observability and governance discipline applies to agent runs. We bring fifteen years of production-ML practice into agent operations directly, which is why our AgentOps offering does not feel like a 2024 startup invented it.
The maturity model matters more than the tool decision. We see four stages in practice: ad-hoc (notebooks shipped by analysts, no production discipline), repeatable (a deployment pattern that works, fragile to staff changes), managed (a platform team that owns delivery and incident response), and optimised (a discipline that improves on a cadence, with FinOps and governance integrated). We assess where the organisation sits and design the engagement to move it one stage, not three. Maturity is a programme, not a project.
The agentic-ML hybrid is the near-term future of every serious MLOps practice. Predictive models will sit alongside agent runtimes in the same production estate. The platform team needs to operate both with the same fluency, because the customer does not care which engine produced the decision; they care that the decision was right and that the audit trail is intact. We build MLOps practices today with that hybrid future baked in.
Architectural patterns we deliver repeatedly: Dataiku on Kubernetes (or Azure Kubernetes Service) for compute elasticity, model registries that integrate with the deploying CI, feature stores that survive schema migrations, evaluation harnesses that can be triggered by drift signals, and audit logging that survives a regulator visit.
[ HOW WE DELIVER THIS ]
How we deliver this
Strategy designs the operating model and the platform decision. Build implements the platform. Operate runs the production estate. Managed Service is the natural endpoint for organisations that want the outcome without standing up the team.