abeebaMLOps Pipeline Setup
Models that live in notebooks don't survive contact with production. We build the pipelines that take a model from experiment to monitored production service, with versioning, evaluation gates and safe rollouts.
What's included.
Experiment tracking & registry
Every dataset, run, metric and model version recorded and reproducible.
CI/CD for models
Automated training, evaluation gates and canary or shadow deployments.
LLM evaluation
Regression suites for prompts and models, including quality, safety and cost checks.
Production monitoring
Latency, drift, cost and quality monitoring with alerts routed to the right team.
How the engagement runs.
Map the lifecycle
Document how models are built, approved and shipped today and where it breaks.
Build the platform
Stand up tracking, registry, orchestration and serving on your infrastructure.
Onboard first models
Move one or two real models through the pipeline end to end.
Scale the practice
Templates, documentation and training so every team ships the same way.
What you keep at the end.
- Running MLOps platform in your environment
- Pipeline templates for new models
- Evaluation and monitoring dashboards
- Team training sessions and documentation
Tools we commonly use
We pick tools to fit your environment and team. These are the ones we use most often.
Do you work with our existing tools?
Yes. We extend what you already use where it makes sense rather than replacing everything.
Does this cover LLM applications as well as classic ML?
Yes. Prompt versioning, evaluation datasets and tracing for LLM apps are part of the platform.
How long until our first model is in the pipeline?
Usually within five to seven weeks of kickoff.
Often combined with.
Private & On-Prem LLM Deployment
Run open-weight models inside your own servers, VPC or air-gapped network.
Learn moreGPU Infrastructure Optimization
Cut inference and training cost by raising utilization across your GPU fleet.
Learn moreCloud-to-AI Migration
Move legacy workloads and data onto AI-ready infrastructure without downtime.
Learn moreReady to run AI on your own terms?
Book a 30-minute consultation with an infrastructure engineer. You'll leave with a clear view of options, costs and next steps, whether or not you work with us.