MLOps Industrialization on AWS

A model that works once is a result. A model with a pipeline, a registry, monitoring and a retraining path is a capability your organization owns. Many teams have the first and not the second: models live in notebooks, nothing reaches production, or a model is in production and nobody knows whether it is still correct. Retraining happens by hand, irregularly, or not at all.

What DinoCloud does

DinoCloud industrializes the lifecycle of your machine learning or generative models on AWS, typically using Amazon SageMaker AI capabilities such as SageMaker Pipelines, Model Registry and Model Monitor. Typical scope includes:

• A current-state review of your stack, version control, pipelines, processes and stakeholders
• Reproducible, versioned and parameterized training pipelines
• Inference pipelines and a serving path
• A model registry with versioning and promotion and rollback between environments
• Evaluation and quality gates enforced in the pipeline before a model is promoted
• Monitoring and alerting for model performance, drift and cost
• A defined retraining trigger and process, including who decides and who approves
• Model governance: ownership, sign-off and a stakeholder map for the lifecycle
• Handover with a runbook and a named operating owner

Governance is part of the delivery, not an afterthought. A model lifecycle is not only infrastructure; it depends on who owns each model, who signs off promotion and who operates it. DinoCloud maps those roles with you so the lifecycle survives after handover.

Who it is for

Data science and ML teams on AWS with at least one model built or in development that want it in production and dependable, and organizations that have delivered one model successfully and want the next ones to start without repeating the effort.