Loading...
Loading...
Found 2 Skills
End-to-end data science and ML engineering workflows: problem framing, data/EDA, feature engineering (feature stores), modelling, evaluation/reporting, plus SQL transformations with SQLMesh. Use for dataset exploration, feature design, model selection, metrics and slice analysis, model cards/eval reports, experiment reproducibility, and production handoff (monitoring and retraining).
Data engineering, machine learning, AI, and MLOps. From data pipelines to production ML systems and LLM applications.