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 built on large language models, the same discipline is often called LLMOps.
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.
MLOps case studies
Common questions about MLOps
What is the difference between MLOps and LLMOps?
MLOps covers the deployment, versioning, monitoring and retraining of machine learning models. LLMOps applies the same discipline to systems built on large language models, adding prompts, retrieval and evaluation sets to what is versioned and monitored. We provide both, for models and for agents.
What does MLOps monitor in production?
Monitoring covers quality against the evaluation set, cost per request, latency, errors and shifts in the data a system receives. Alerts reach the people who own the response, and documented procedures cover rollback and retraining.
How is a faulty model or prompt rolled back?
Every model, prompt and evaluation set is versioned, and each release records the versions it contains. A rollback restores a known version through the same pipeline, and the restored version is checked against the evaluation set before users rely on it again.
More in Operate
-
AI Integration & Implementation
Integration of AI systems into existing enterprise platforms with the security, identity and observability controls those platforms require.
-
AI Business Automation
Governed agents that carry operational and administrative workflows, with defined decisions reserved for a person.
-
Data Science & Analytics
Forecasting, anomaly detection, experimentation and decision analytics that give leadership a quantified basis for operational choices.
Discuss this service on a call.
Describe the objective and the systems it affects, and we will outline how an engagement would proceed.