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.
Practical, code first guide to cost effective model monitoring using Prometheus and MLflow: choose metrics, instrument exporters, set SLOs, and optimize retention.
Hands on guide to detecting and responding to data drift in production ML using SQL checks, Python monitoring, alerting, and auto remediation.
Hands-on Mathematics guide: implement and optimize sparse matrix algebra in Python with SciPy, PyTorch, and GPU libraries for production ML pipelines.
Practical Power Query data prep for ML in Excel: M recipes for cleaning, normalization, feature extraction, parameterized refresh, and export to CSV or SQL.
Hands on guide to building incremental feature refreshes with SQL and dbt for production ML pipelines: partitioning, CDC merge patterns, materializations, testing, tuning.
A benchmark driven guide to optimize, debug, and scale Python ETL and ML preprocessing with Dask and Ray.