Detecting Data Drift in Production ML Pipelines with SQL
Hands on guide to detecting and responding to data drift in production ML using SQL checks, Python monitoring, alerting, and auto remediation.
AI, data science, and programming for small business applications and large enterprises
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Hands-on Mathematics guide: implement and optimize sparse matrix algebra in Python with SciPy, PyTorch, and GPU libraries for production ML pipelines.
Power Query Data Prep for ML in Excel: Hands on Recipes
Practical Power Query data prep for ML in Excel: M recipes for cleaning, normalization, feature extraction, parameterized refresh, and export to CSV or SQL.
Incremental Feature Refresh in SQL and dbt for ML
Hands on guide to building incremental feature refreshes with SQL and dbt for production ML pipelines: partitioning, CDC merge patterns, materializations, testing, tuning.
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A benchmark driven guide to optimize, debug, and scale Python ETL and ML preprocessing with Dask and Ray.
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A practical 10-step incident response checklist tailored for Calculus teams in small and mid-size businesses.