laxmi-narayan-87/ml-engineering-suite
A unified machine-learning engineering toolkit spanning dataset quality, model development, hyperparameter optimization, MLOps pipelines, deployment, monitoring, and rollback.
Use when designing annotation guidelines, auditing training data, sampling examples for labeling, measuring annotator agreement, or preparing versioned ML datasets.
Use when setting up grid/random/Bayesian hyperparameter search, cross-validation, early stopping, model comparison, or an optimization budget for ML models.
Use when creating or improving repeatable machine-learning pipelines, CI/CD for models, orchestration DAGs, model registry workflows, deployment gates, or production monitoring.
Use when packaging models, building batch or online inference, exposing prediction APIs, designing rollout/rollback, or checking production ML reliability.
Use when designing, training, validating, comparing, or debugging machine-learning models and the datasets/features they depend on.