The situation
A team manually deploys a forecast model without a dependable validation record.
Production MLOps
MLOps engineers the lifecycle of machine-learning models: data and model validation, versioned deployment, monitoring and controlled change. It connects a model experiment to a maintainable production service.
- Define the lifecycle
- Engineer release and validation
- Plan operations
Is this right for you?
When this is the right work.
A predictive model works in a notebook but release, reproducibility, monitoring or retraining responsibilities are unclear.
The first conversation establishes the environment, the constraint and who will accept the result. We agree a bounded starting point before expanding the engagement.
Scope
What DotConn delivers.
- 01
Define the lifecycle
Map data inputs, model versions, evaluation and the criteria for promotion.
- 02
Engineer release and validation
Build reproducible pipelines and checks for the model-serving path.
- 03
Plan operations
Monitor agreed quality and drift signals, evaluate compute costs and define review or retraining triggers.
Deliverables
What you receive.
Acceptance criteria identify the checks, records and handover needed for the agreed scope.
- Model lifecycle and acceptance design
- Versioned validation and deployment pipeline
- Monitoring and operating runbooks
In practice
A practical example.
Our approach
We establish a repeatable release path and agree what evidence is required to promote a new model.
The useful result
The operating owner can trace which version is running and when it should be reviewed.
Responsibility
Agree the responsibility.
DotConn
Assesses and delivers the technical work in the agreed scope, with reviewable changes and handover. Continuing operation requires a separately defined managed-service scope.
Your team
Provides authorized access, business priorities and accountable decision-makers; approves policy choices and accepts the agreed result.
Set this boundary early
Monitoring does not establish that retraining is always appropriate. Data rights, model suitability and acceptance thresholds are customer-specific decisions.
FAQ
Questions worth resolving.
Technical reading
How is MLOps different from LLMOps?
MLOps covers machine-learning systems broadly, including predictive models. LLMOps gives specific attention to prompts, retrieval, model-provider changes and generative behavior.
Pricing, timing and how to prepare are covered in working with DotConn.
Connected work
Services that work alongside this.
Production LLMOps
LLMOps manages changes and operations for language-model applications. Prompts, model choices, retrieval configuration and tool permissions can all affect behavior and need a reviewable release path.
ExploreMonitoring & Observability
Observability uses signals such as metrics, logs and traces to help explain system behavior. Monitoring turns selected conditions into checks and actionable alerts.
ExploreCloud Cost Optimization & Commitment Advisory
Cloud cost advisory evaluates architectural and purchasing choices against workload requirements. It should explain the operational trade-off behind a potential reduction in spend.
Explore