Learn the work behind Data, AI & Forward Deployed Engineering
Practical explanations, career decisions and reproducible workflows. Read the reasoning, inspect the evidence and follow the next skill into a real programme.
Detect overfitting in a small instruction dataset
Training loss can continue to fall while unseen behaviour gets worse. With a small instruction set, duplicates, template shortcuts and label inconsistencies make this especially easy to miss.
Detect prompt injection inside an uploaded business document
Prompt injection occurs when untrusted content attempts to redirect an assistant's behavior. In an uploaded business document, the suspicious content may ask the assistant to ignore its task, alter a reported number or c
Detect schema drift in a third-party integration
A provider can add fields, change a number to a string or remove a unit. If the adapter accepts drift silently, model context and downstream calculations can change without an obvious error.
Detect training-serving skew with schema contracts
Training-serving skew occurs when production inputs differ from the data or transformation contract used to train the model. A schema catches structural failures before they become plausible-looking predictions.
Detect unstable customer segments after retraining
Cluster labels have no inherent identity. A retrained model can call the same profile “cluster 2” instead of “cluster 0.” Counting raw label changes without alignment can report migration that is only renaming.
Diagnose a conversion drop with a segmented checklist
A lower overall conversion rate can result from weaker conversion within comparable groups, a shift toward groups that normally convert less, or a measurement problem. Start by separating those possibilities. An aggregat
Diagnose a RAG failure from its intermediate evidence
“The chatbot hallucinated” is not a diagnosis. A RAG answer can fail because the source was absent, filtered out, poorly chunked, not retrieved, dropped from context, ignored during generation or cited incorrectly.
Diagnose a slow Power BI page with performance evidence
Diagnose a slow report by reproducing a specific interaction and measuring where time is spent. “The dashboard is slow” is too broad to guide a useful change. A delayed source query, expensive measure, overloaded page an
Distillation: define what the smaller model must preserve
Distillation is useful only when the smaller system preserves the behaviours the application needs. Define those behaviours before generating teacher outputs or comparing speed.
Dockerize a model service with a reproducible environment
A Dockerfile describes how to build an image. It does not prove the image builds, runs, passes a vulnerability scan or serves correct predictions. Keep specification review and executed container evidence separate.
Document a business rule change in historical reporting
When a business rule changes, record the old definition, new definition, effective date and treatment of historical values. A chart that combines old-rule history with new-rule current values can show a change that partl
Document a data dependency that can invalidate a model
A model artifact can remain byte-for-byte unchanged while its predictions become invalid. An upstream team may change a unit, event definition, timestamp or late-data policy. Those changes alter the population or feature
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