Perspectives on data engineering, MLOps, and AI systems from the field.
For decades, professional design skills created a significant barrier for software engineers building products independently. Creating polished user interfaces, cohesive brand identities, and intuitive user experiences required specialized expertise that most engineers lacked. Solo developers and small teams faced a difficult choice: compromise on design quality or hire expensive design professionals.
Your team built the demo in a week. The CEO loved it. That was four months ago. The LLM-powered feature is still not in production. The demo-to-production gap for LLMs is wider than for traditional ML.
Most ML models don't need real-time serving. A batch job that runs overnight and writes predictions to a database covers 80% of use cases: churn scores, recommendation lists, risk ratings, demand forecasts. For the other 20%, you need predictions at request time.
Common patterns we see in enterprise data strategy engagements, and the architectural decisions that separate success from stagnation.
Every data project in your organisation starts the same way. A data engineer spends two weeks setting up infrastructure: provisioning compute, configuring storage, wiring up orchestration, writing boilerplate ingestion code. The project is unique. The setup is not.
Your ML team requested four A100 GPUs for training. They got them. Utilisation is 12%. The GPUs sit idle 20 hours a day while your cloud bill climbs five figures a month. This isn't unusual.
Every tutorial makes Retrieval-Augmented Generation look simple. Embed your documents, store them in a vector database, retrieve the top-k chunks, stuff them into a prompt. Demo works in an afternoon.
Every data team has a quality dashboard. Red and green indicators. Freshness checks. Row count comparisons. The dashboard says green. Then a downstream report serves wrong numbers to the board. Dashboards tell you something already broke.
A practical framework for assessing where your ML operations stand today and what it takes to reach continuous, reliable model deployment.
Two paradigms. A mountain of vendor marketing. Conference talks that use both terms interchangeably. And a data team that just needs to know: which one do we actually build?