Machine Learning & Decision Systems
Turn business decisions into model-informed actions, then make the learning, limitations, and engineering evidence clear enough to review and trust.
Problem classes
ML systems and LLM systems solve different kinds of problems.
They are complementary. The distinction helps a reader choose the right evidence, evaluation method, and operational controls for the work.
Focuses on prediction, ranking, action selection, policy value, and measurable outcomes from structured evidence.
Typical question: Which eligible action should be selected for this context, and what evidence supports that decision?
Focuses on language reasoning, content generation, tool use, orchestration, review, and controlled execution.
Typical question: How should AI-assisted work be routed, checked, evidenced, and kept within human authority?
Explore LLM & Agent SystemsNBO-NRT Decision Intelligence
A governed Telco learning case study for choosing a relevant eligible offer for the current customer context while preserving evidence, constraints, and learning boundaries.
Enter the NBO-NRT AIOS CockpitBusiness problem
Which eligible offer should be selected for a customer at the current decision point?
Action space
BUNDLE, DATA, LOYALTY, ROAMING, and VOICE in the bounded synthetic experiment.
Technical progression
Reward modeling → contextual ranking → policy learning → offline evaluation.
Engineering progression
Unity Catalog → MLflow persistence → artifact recovery → traceable evidence.
Evidence boundary: The NBO-NRT work is a governed synthetic learning and MLOps evidence case study. It does not establish operator business truth, production uplift, online safety, or production readiness.
Three lenses
One system, three reader questions.
Each knowledge item has one primary home. Related lenses link to it instead of duplicating or silently changing the evidence.
ML capability map
From prediction to governed decisions.
The progression shows how the work moves from estimating outcomes toward selecting actions and governing the evidence lifecycle.
- 1
Supervised ML
Learn patterns from labeled outcomes.
- 2
Reward / Response Modeling
Estimate expected response for a context and action.
- 3
Context × Action Modeling
Represent how different actions behave in different customer contexts.
- 4
Contextual Decisioning
Choose one action for the current context under explicit constraints.
- 5
Policy Learning
Define how action selection should balance reward, support, and continued learning.
- 6
Offline Policy Evaluation
Estimate policy value from governed logged interactions before online use.
- 7
MLOps / Evidence
Persist, recover, trace, validate, and govern data and model artifacts.
See how the evidence evolved in practice.
Open the flagship Cockpit for experiment history, current boundaries, and the next gate.