Applied AI, from data platform to model in production

Data science focuses on extracting insights from data. AI engineering focuses on building production systems that use AI, integrating models into applications, building infrastructure to serve predictions at scale and maintaining reliability over time.

MLOps is the set of practices for deploying, monitoring and maintaining machine learning models in production. Without it, models silently degrade as real-world data diverges from training data.

A Feature Store is a centralized repository for computed inputs used to train and serve ML models. It computes features once, serves them consistently for both training and inference, and prevents data leakage.

Data lineage tracks where each piece of data came from, what transformations it underwent and where it was used. In regulated sectors, this is often a compliance requirement.

Responsible AI covers fairness, accountability, transparency and safety. It includes bias detection, model explainability, audit trails and guardrails preventing harmful outputs.

The gap is primarily an engineering problem. Prototypes use clean, static datasets; production systems receive messy real-time data, must meet latency budgets, integrate with existing infrastructure and handle edge cases.

Prompt engineering shapes behavior through instructions at inference time — fast to iterate but limited. Fine-tuning modifies model weights using new training data, enabling deeper domain specialization and more consistent behavior.