Calculus Guide: Autodiff vs Numerical Differentiation
Practical Calculus comparison for MLOps engineers: autodiff versus numerical differentiation, stability, performance, Hessian techniques, and production patterns in PyTorch, JAX, and NumPy.
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Cost Effective Model Monitoring with Prometheus and MLflow
Practical, code first guide to cost effective model monitoring using Prometheus and MLflow: choose metrics, instrument exporters, set SLOs, and optimize retention.
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.
Efficient Sparse Matrix Algebra in Python for Mathematics
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.