I build processes for testing, deploying and monitoring ML models. I also audit existing infrastructure and document the agreed changes.
The diagram shows six stages of an ML system: data, development, testing, deployment, monitoring and scaling.
The scope can cover an audit of existing infrastructure, implementation of agreed changes or training for a team.
This work prepares experimental code for production or changes an ML platform already in use.
A consultation can cover deployment failures, monitoring gaps, rising costs or an architecture review.
Training is for teams that operate ML systems but need practice with deployment and maintenance.
Before work begins, we agree the deliverables, scope, schedule and price. An audit can be commissioned separately; its findings are used to plan further work.
In your first message, state the goal and what is not working. I will ask about the technical environment and constraints.
The proposal lists the work and its deliverables. Price and schedule depend on the scope and current environment.
I complete the agreed work or deliver training. The documentation records the changes and how to maintain them.
I add a component only when it solves an agreed problem. Each change must be traceable, with its maintenance documented.
Ignis Centralis provides MLOps infrastructure audits, implementation, consulting and training.
Rokas reviews each enquiry, agrees the scope, carries out the work and hands the documentation to the team.
Yes. The scope can be an architecture review, deployment-process analysis, monitoring setup or cost review. A full platform replacement is not required.
Yes. I first assess the current infrastructure, team processes and constraints. We then decide what to keep, what to change and in what order.
An audit can be commissioned separately. I provide a current-state assessment and action plan. Implementation is scoped separately.
State which system you use, what is not working and the result you need. You do not need to prepare a technical brief.