ai-ml-development

To create an eval suite for machine learning model changes, design should-pass and should-fail test cases, run them against current and previous versions, and generate prompt or fine-tune diffs with eval deltas to measure performance differences.

The best way to implement rollback for model serving is to define deterministic fallbacks tied to pinned model versions, establish inference SLAs, and rehearse rollback by switching version pins to ensure safe recovery.

To ensure reproducibility in machine learning experiment tracking, pin datasets and code while recording dataset hashes, code hashes, hyperparameters, and metrics for reliable baseline comparisons.

Choosing between fine-tuning vs RAG vs prompting depends on eval results, official-source grounding requirements, and safety constraints, turning model and prompt changes into measurable experiments to verify correctness before release.

To build model cards with safety and fairness notes, document training data sources, intended use cases, limitations, and fairness considerations, while verifying outputs for PII and policy risks prior to deployment.

To define inference SLAs for machine learning serving, specify P50, P95, and P99 latency targets alongside cost per call, adding deterministic fallbacks to maintain reliability and rollback capabilities.