This Ml Systems For Engineering Problems eBook from 3D-LABS is written for engineers and students who want a clear, practical reference they can apply immediately.
ML Systems for Engineering Problems covers the complete ML system pipeline for engineering – data acquisition, feature engineering, model selection, hyperparameter tuning, edge/cloud deployment, and MLOps lifecycle management for mechanical and industrial applications.
What standards does this book reference?
ISO/IEC 25010:2023 software quality, ISO/IEC 42001:2023 AI management, Google MLMD for experiment tracking, and ONNX Runtime v1.16 for cross-hardware model portability.
What ML algorithms are covered for engineering regression?
Supervised regression: Linear (Lasso/Ridge), XGBoost/LightGBM gradient boosted trees, Random Forest, Gaussian Process (for uncertainty quantification). Time series: LSTM, TCN. Metrics: RMSE, MAE, R-squared, PICP for uncertainty-aware models.
What MLOps tools are used?
MLflow 2.8 (experiment tracking/registry), DVC (dataset versioning), Apache Airflow 2.7 (orchestration), FastAPI (model serving REST), Prometheus+Grafana (monitoring), ONNX Runtime for edge deployment on Jetson/Raspberry Pi.
How do I access this book after purchase?
eBook PDF: Instant download via email and 3D-LABS dashboard. Paper Book: Printed and shipped to India.
What’s Included
An instant PDF download covering the core concepts, practical examples, and key references — ready to read on any device.

