Machine Learning Engineering

ML infrastructure connects the data, training, and serving components that turn a model into a production system. Its design depends on the workload: a fraud model answering in milliseconds needs a different setup from a demand model that runs once a day.

A prototype can show that a model works on historical data. Machine learning model deployment turns that experiment into a repeatable production system, with versioned code, controlled data flows, suitable infrastructure, monitoring, and a clear response when conditions change.

The process starts once the model has passed its initial evaluation. From that point, teams need to prove that the model can run reliably with real inputs, within the required latency, security, cost, and governance constraints.

RAG architecture connects a generative AI model to external knowledge. The system retrieves relevant documents or passages, adds them to the model’s context, and uses that evidence to generate a response.

In this article, it is explained how a RAG system works in practice, from document ingestion and embeddings to semantic search, context selection, generation, and evaluation.

This article demystifies the three primary model families used in business forecasting regression, time series, and ensemble methods explaining what each does, when it works, and when to look elsewhere.