Capability guide
Production AI combines versioned behavior, evaluation, deployment and runtime visibility. Predictive models and generative applications share operating needs but require different validation and failure-handling choices.
Make AI changes reviewable. The right starting point depends on what already exists, which decisions are open and the people who will operate or use the result.
Engineer model deployment pipelines, prompt versioning and automated evaluation guardrails.
Monitor hallucinations and drift; review GPU and inference costs against workload quality and latency requirements.
A focused assessment can establish the unknowns. Implementation delivers an agreed change. Ongoing services and embedded specialists require their own responsibility and coverage terms.
Choose the topic you need to inspect. Each page explains the specific work, a practical example, deliverables and its operating boundaries.
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.
A predictive model works in a notebook but release, reproducibility, monitoring or retraining responsibilities are unclear.
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.
A generative AI prototype is becoming a production dependency, but nobody can confidently compare releases or explain quality and inference costs.
The exact outputs follow the engagement. This capability group can include versioned releases, evaluation gates, monitoring & inference cost model.
Agree the acceptance evidence, repository or record location and next operating owner before the work begins. Exceptions and unfinished dependencies remain visible in the handover.
Coordinates the agreed assessment, implementation or service activities, directly or with the required specialist support. The scope defines the decisions, checks and records to be delivered.
Provides business priorities, authorized access and accountable reviewers. Customer-directed individual staffing remains distinct from a DotConn-coordinated team or managed service.
AI evaluation reduces uncertainty; it does not guarantee correct answers. Production application AI is distinct from workplace AI access and governance in Managed TechOps.
MLOps covers model systems broadly, including predictive AI. LLMOps focuses on language-model applications, prompts, retrieval, tools and generative behavior.
Yes. Start with a bounded requirement, then connect adjacent work only when the dependencies or intended outcome justify it. Your enquiry can name the specific capability and current constraint.