Pipelines and reproducibility
Make training repeatable by anyone on the team, not only by the person who ran it first.
- Data and feature versioning
- Training pipeline automation
- Experiment tracking
- Artifact and model registry
MLOps & LLMOps
A model in production is software that decays.
Deploy, monitor, and retrain models with the same release discipline as application code: versioned artifacts, drift detection, evaluation gates, and tested rollback procedures.
Inside the delivery
A model lifecycle that can be reproduced and operated
Version datasets, features, training configuration and evaluation results together. The flow below shows the main delivery stages and the evidence produced at each step.
- 01
Training lineage
Version datasets, features, training configuration and evaluation results together.
Output
Reproducible training pipeline and lineage records
- 02
Registry and release
Package approved model versions with compatibility requirements and release gates.
Output
Model registry conventions and release workflow
- 03
Serving and monitoring
Deploy inference with health, latency, usage and relevant quality signals.
Output
Serving configuration and monitoring specification
- 04
Review and retrain
Define drift investigation, retraining approval and recovery to a compatible model version.
Output
Retraining policy and model recovery runbook
Controls across the workflow
- Dataset lineage
- Model approvals
- Serving health
- Rollback compatibility
Decisions that shape the scope
- Should drift automatically trigger retraining?
- Not necessarily. Drift may signal a data defect or a changed business process. Investigate before retraining, then evaluate the candidate and approve its release against the current baseline.
- How is MLOps different from DevOps?
- The release mechanics are similar; the failure mode is not. Application code fails visibly, while a model keeps returning confident answers as it degrades. MLOps adds the data versioning, drift monitoring, and evaluation gates that make that silent decay detectable.
Before you commit
Is this the right engagement?
- What we need from you
- Training code, dataset versions, model artifacts, inference requirements, feedback availability and current deployment infrastructure.
- How you accept the work
- Define drift investigation, retraining approval and recovery to a compatible model version. Acceptance records the tested scope, unresolved issues and the owner's decision.
- Scope & alternatives
- Not necessarily. Drift may signal a data defect or a changed business process. Investigate before retraining, then evaluate the candidate and approve its release against the current baseline.
Overview
Training the model is the short part
Production models need versioned data and artifacts, observable serving infrastructure and clear decisions about retraining. We connect these parts through release gates and tested recovery procedures. Recovery time depends on model compatibility, infrastructure and the agreed operating requirements.
- 01Versioned data, features, and model artifacts
- 02Deployment, serving, and rollback
- 03Drift, quality, and cost monitoring
- 04Evaluation gates and retraining triggers
Capabilities
Engineering scope and deliverables
Select the work that addresses your constraint. Responsibilities and acceptance criteria are agreed before delivery.
Deployment and serving
Get models into production behind an interface the rest of the system can depend on, with the release controls any other service would have.
- Batch, real-time, and streaming serving
- Shadow and canary releases
- Version promotion and rollback
- Latency and cost targets
Monitoring and evaluation
Watch the things that actually degrade: the inputs, the outputs, and the business signal the model is supposed to move.
- Input and prediction drift
- Ground-truth and delayed-label evaluation
- Quality regression gates
- Alerting and on-call integration
LLM operations
Apply the same discipline to prompted and retrieval systems, where the model is external and the failure modes differ.
- Prompt and configuration versioning
- Evaluation sets and regression runs
- Token cost and latency monitoring
- Provider and model migration
Integrations
Selected for your environment
Tools are chosen around your existing systems, access requirements and operating constraints.
Cloud platforms
Databricks
CI/CD pipelines
Observability tools
OpenAI and Azure OpenAI
Data platforms
Frequently asked questions
Questions to resolve before starting
01Should drift automatically trigger retraining?
Should drift automatically trigger retraining?
Not necessarily. Drift may signal a data defect or a changed business process. Investigate before retraining, then evaluate the candidate and approve its release against the current baseline.
02How is MLOps different from DevOps?
How is MLOps different from DevOps?
The release mechanics are similar; the failure mode is not. Application code fails visibly, while a model keeps returning confident answers as it degrades. MLOps adds the data versioning, drift monitoring, and evaluation gates that make that silent decay detectable.
03Does this apply if we only use hosted LLMs?
Does this apply if we only use hosted LLMs?
Yes, and it is often more urgent. You do not control the model, so prompt and configuration versioning, evaluation sets, and provider migration planning are the only levers you have when behavior changes underneath you.
04How often should a model be retrained?
How often should a model be retrained?
On a trigger rather than a schedule where possible: measured drift, a quality threshold breach, or a known change in the upstream process. A calendar retrain that is not gated on evaluation is as likely to make the model worse as better.
05What if we have models in production with none of this?
What if we have models in production with none of this?
That is the usual starting point. We normally begin with monitoring and rollback, because they reduce risk immediately, then work backwards into reproducible training once the current behavior is measurable.
06What should we prepare for the first technical discussion?
What should we prepare for the first technical discussion?
Training code, dataset versions, model artifacts, inference requirements, feedback availability and current deployment infrastructure.
07What evidence is available at handover?
What evidence is available at handover?
The agreed delivery includes retraining policy and model recovery runbook. Define drift investigation, retraining approval and recovery to a compatible model version.
08How is the engagement estimated?
How is the engagement estimated?
We review the available inputs before estimating: Training code, dataset versions, model artifacts, inference requirements, feedback availability and current deployment infrastructure. The proposal identifies dependencies, review milestones and excluded work; the scope determines the schedule.
Related
Related services and delivery options
Start a conversation
Discuss your next technical step
Share your current situation and the constraint you need to resolve. We will use the discovery inputs above to define a practical scope for MLOps & LLMOps.