Technology
MLOps Services — Production Machine Learning & LLM Operations
MLOps platform engineering — pipelines, model registries, evaluation, monitoring, and incident response for ML and LLM systems.
What we build with MLOps
- Training pipelines on SageMaker, Vertex AI Pipelines, or Kubeflow
- Model registries with MLflow, SageMaker Model Registry, or Vertex
- Evaluation harnesses for ML and LLM systems
- Drift detection, performance monitoring, and alerting
- Feature stores: Feast, Tecton, or warehouse-backed
- Model deployment with shadow traffic, A/B, and gradual rollouts
Why DiveScale
Built by engineers who ship MLOps in production
MLOps is what separates a notebook from a product. DiveScale designs and operates ML platforms that handle the unglamorous parts: reproducible training, model lineage, eval-gated deploys, drift monitoring, and the incident response that keeps stakeholders trusting the system.
We work across SageMaker, Vertex AI, Azure ML, and open stacks (Kubeflow, MLflow, Argo). The choice depends on where your data lives, what your engineering team already runs, and how much custom orchestration you actually need.
For LLM systems we extend the same discipline: prompt versioning, eval suites, traces in Langfuse, and rollback paths when a new model version regresses on your data.
MLOps use cases we deliver
How we deliver
Our MLOps delivery process
- 01
Platform audit
We map current ML workflows, identify the bottlenecks, and propose a target architecture grounded in what your team can operate.
- 02
Pipelines + registry
We build reproducible training pipelines and a model registry so every production model has a paper trail.
- 03
Evaluation & monitoring
Eval-gated deploys, production monitoring, and alerting on drift and quality regressions.
- 04
Operate or hand off
We stay on as the platform team or train your engineers with runbooks and on-call rotation.
Related technologies
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Learn moreMLOps — Frequently Asked Questions
Only when training/serving skew is a real risk — usually at the point where multiple models share features or when online inference happens at scale. For smaller teams a warehouse + careful pipeline often suffices.
