ML Data Pipelines Curriculum

A new public curriculum is trying to teach a part of machine learning interviews that often sits between coding drills and full production work: data pipelines and feature engineering. (x.com) In machine learning, a pipeline is the sequence of steps that moves raw data into training data, trains a model, and sends that model into production. Google’s “Rules of Machine Learning” says the pipeline includes gathering data, creating training files, training models, and exporting them to production systems. (developers.google.com) Feature engineering is the step where teams turn messy real-world records into model inputs, like converting a customer’s order history into counts, averages, or recent activity. Andriy Burkov’s *Machine Learning Engineering* lists data collection and preparation, feature engineering, model training, deployment, serving, monitoring, and maintenance as stages of the machine learning lifecycle. (mlebook.com) (studylib.net) That makes the curriculum notable for interview prep because it targets the engineering layer around models, not just model math. Google’s guide says many production machine learning problems are engineering problems and that “most of the gains come from great features, not great machine learning algorithms.” (developers.google.com) The production side matters because models can fail when training data and live data stop matching. Google says it has observed training-serving skew in production systems, and Feast, an open-source feature store, is built around making features available for both historical training and low-latency serving. (developers.google.com) (docs.feast.dev) A feature store is a shared system for defining, storing, and serving model inputs so teams do not rebuild the same logic in separate batch and real-time code paths. Feast describes its core as an offline store for historical feature extraction and an online store for low-latency production serving. (docs.feast.dev) (feast.dev) That topic set lines up with the kinds of questions candidates now see for machine learning-adjacent roles, where interviewers ask about scale, data quality, leakage, drift, and serving constraints rather than only loss functions. Interview prep sites aimed at Meta, Google, Amazon, Airbnb, Uber, Netflix, and similar companies now frame feature engineering around production tradeoffs and reliability. (datainterview.com) Burkov’s post packages that material as a curriculum rather than a single article or lecture, which gives candidates a sequence through theory, algorithms, and systems design. His public *Machine Learning Engineering* materials already organize the field from data collection through monitoring, and the new post extends that practical framing to interview preparation. (x.com) (mlebook.com) The result is a study path for candidates who can already solve coding problems but need to explain how data becomes a reliable model input in production. That is the gap the curriculum is trying to cover. (x.com)