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.
Run an AI incident tabletop exercise
A tabletop tests decisions and coordination before a real incident removes time and certainty. The value comes from realistic injects and observed gaps, not from a polished discussion where every answer is known.
Run an open model locally with explicit resource limits
Local inference removes a hosted call, but it does not remove capacity, licensing, privacy or safety responsibilities. Admission control should reject work that cannot fit within known bounds before memory is exhausted.
Sampling bias in a customer satisfaction survey
A satisfaction mean describes the people who supplied usable responses unless the design and adjustment support a broader target population. Highly engaged customers may respond more often than quiet or dissatisfied cust
SARIMA for recurring seasonal demand
SARIMA extends ARIMA with seasonal autoregressive, differencing and moving-average terms at a declared period. For monthly data with annual recurrence, the period is 12. A seasonal AR term can relate a month to the same
Save and reload a neural model with equivalent predictions
A model that worked in a training process is not deployable until its artifact can reconstruct the same inference function. Save all learned parameters and preprocessing, reload them in a fresh path, and compare predicti
Schedule a Python report with observable failures
A scheduler starting a process does not establish that the report succeeded. The process must return a meaningful exit status, retain an attempt log and produce a verifiable output artifact. Monitoring also needs to dete
Schema evolution without silently changing feature meaning
A pipeline can keep running after a schema change and still become wrong. The dangerous case is a field that retains its name and type while its business meaning changes. If amountcents starts carrying rupees, every reco
Scope an enterprise AI pilot with stop-go criteria
A pilot is a decision instrument, not a small production launch. Its job is to reduce the largest uncertainties cheaply enough that a named sponsor can continue, change direction or stop. If the scope says only “build a
Seasonal naive forecasting as a serious baseline
A complex forecasting model has not earned its place merely because it produces a forecast. It should beat a rule that a planner can understand, reproduce and operate. For recurring monthly demand, one of the strongest s
Secure model artifacts and avoid untrusted deserialization
Common Python model formats built on pickle can execute code while loading. Treat an artifact as executable supply-chain input. Only load from controlled sources after verifying identity, integrity and expected environme
SELECT and WHERE: build a reproducible sales extract
SELECT chooses the columns or expressions returned by a query. WHERE determines which input rows qualify. For a business extract, those two decisions define both the information delivered and the population it describes.
Semantic caching: test incorrect cross-user reuse
A semantic cache can save time and cost by reusing an answer for a similar request. Similar wording does not imply equal authority, tenant, policy version or source visibility. A cache key that ignores those boundaries c
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