Describe the key stages of a typical ML lifecycle

WHAT THIS TESTS: This question checks if you understand machine learning as a repeatable engineering process rather than an ad-hoc modeling exercise. Interviewers want to see that you can articulate the full chain from business value to production operations, including the feedback loops that keep a model healthy after launch. At the senior level, simply naming stages is not enough; you must show awareness of handoffs, quality gates, and operational risks between each phase.

A GOOD ANSWER COVERS: A strong response names five to six distinct stages in order and adds one concrete concern per stage. First, business goal definition and ML problem framing, where you translate a business metric into a model objective and decide if ML is even necessary. Second, data engineering and validation, including ingestion, schema checks, missing value handling, and feature store integration. Third, model development and experimentation, covering training, hyperparameter tuning, and offline evaluation against holdout sets with proper cross-validation. Fourth, deployment and release, which includes packaging, A-B testing infrastructure, canary rollouts, and scaling inference endpoints. Fifth, monitoring and maintenance, encompassing data drift detection, concept drift, latency and error rate alerts, and automated retraining triggers. Mentioning that these stages loop back to the start shows maturity.

COMMON WRONG ANSWERS: A frequent red flag is jumping directly to neural network architecture or algorithm selection without mentioning data validation or business context. Another weak pattern is describing the lifecycle as a linear waterfall with no iteration; production ML requires continuous feedback. Candidates who omit monitoring entirely or treat deployment as just saving a pickle file to S3 signal a lack of production experience. Similarly, conflating offline model accuracy with business impact suggests shallow understanding of the full pipeline.

LIKELY FOLLOW-UPS: An interviewer may ask how you handle data drift in a production pipeline, what your rollback strategy looks like when model accuracy degrades, or how you version datasets alongside code and models. They might also probe the transition from batch to real-time inference, or ask how you validate features before they enter the training set. Be ready to discuss specific tools for experiment tracking, model registries, and feature stores.

ONE CONCRETE EXAMPLE: Imagine a fraud detection model for an e-commerce platform. The lifecycle starts with defining fraud cost savings as the north star metric. Next, you build pipelines to ingest transaction logs, enforce schema contracts, and compute velocity features in a feature store. You then train a gradient boosted tree, evaluate precision-recall curves on a temporal validation split, and promote the best candidate to a model registry. Deployment uses a canary release on five percent of traffic with shadow mode before full rollout. In production, you monitor feature distributions hourly, trigger retraining when PSI exceeds zero point two, and maintain a rollback button to the previous model version within sixty seconds.