AI Development
Plan how AI systems will be deployed, monitored and maintained after the initial build. We connect model operations and AI-assisted operational analysis to defined service owners, quality measures and recovery procedures.
A working prototype is only one stage of an AI service. Production operation needs controlled releases, versioned configuration, relevant monitoring and a response plan when quality, data or dependencies change. AIOps use cases also need clear rules for how detected anomalies are reviewed and acted on.
Plan environments, release checks, versioning and rollback for the agreed application or model.
Observe relevant quality, response time, usage, cost and data-change indicators.
Assess anomaly detection and incident-triage support against available logs and operational data.
Define alerts, escalation, incident ownership and the process for reviewing improvements.
This service fits organizations moving an AI application into ongoing production or assessing AI assistance for IT operations. Its scope is agreed alongside the existing infrastructure and security responsibilities.
Provide the current deployment architecture, service objectives, model or application details and available monitoring data. We identify the controls needed before planning an operating model and maintenance arrangement.
MLOps manages the lifecycle and operation of machine-learning systems. AIOps applies AI methods to operational data to assist activities such as anomaly detection and incident triage.
Relevant usage, cost, latency and quality indicators can be included in the agreed monitoring scope.
Not by default. The scope defines how findings are reviewed, which actions are permitted and when an operational owner must approve a change.
Tell us about the workflow and outcome you want to improve so we can define a practical next step and a clear project scope.
