Operate
MLOps
Operations that keep models and agents measurable, reproducible and reversible for as long as they run in production.
About MLOps
AI systems degrade in production without maintenance: data distributions shift, providers update models, prompts accumulate untested changes and costs drift, until a system that performed well at release no longer does. MLOps is the set of practices that counters this decline: deployment pipelines, environment promotion, versioning of models, prompts and evaluation sets, monitoring of quality and cost, and procedures for rollback and retraining.
For systems ByteForge built, operations continue after handover where the client requires them, using the pipelines, monitoring and procedures already in place. Where another firm built the system, the work opens with an assessment of the existing pipeline: how models, prompts and data reach production, what carries a version and where failures go undetected. We rank remediation by operational risk and measure each change to the pipeline before it reaches users.
What you receive
- Deployment and promotion pipelines
- Monitoring and alerting for quality and cost
- Model, prompt and evaluation set versioning
- Rollback, retraining and incident procedures
When to engage
Engage when a model or agent is entering production, or when one already in production resists measurement, rollback or reproduction on demand.
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Discuss this service on a call.
Describe the objective and the systems it affects, and we will outline how an engagement would proceed.