The Practical Guide to MLOps and Data Pipeline Automation

Avis Gabe

ISBN 13: 9798296195050

Published by Independently Published, 2025

About this Item

Seller Inventory # LU-9798296195050

Bibliographic Details

• Title: The Practical Guide to MLOps and Data Pipeline Automation
• Publisher: Independently Published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798296195050
• Binding: Paperback
• Condition: New
• Dimensions: 17.78W x 1.4H x 25.4D centimeters 17.78 centimeters width by 1.4 centimeters height by 25.4 centimeters depth
• Weight: 431 grams

About this title

Synopsis

What happens when your best model silently fails in production? What’s the true cost of a brittle data pipeline, or an untraceable deployment?

Every ambitious ML project promises the future, but reality bites: endless manual fixes, untracked changes, model drift, downtime, and costs that spiral out of control. This isn’t a book of hollow theory—it’s a blueprint for surviving, and thriving, in production.

Inside, you’ll discover:

• Foundations of MLOps: Modern principles that power repeatable, reliable machine learning.

• Data Pipeline Automation: Proven methods for data ingestion, validation, modular ETL, and versioning.

• Versioning Everything: Strategies for tracking code, datasets, features, experiments, and models.

• CI/CD for Machine Learning: Concrete steps to automate model delivery using MLflow, Kubeflow, and TFX.

• Automated Testing: How to build quality gates, detect drift, and deploy with confidence.

• Model Lifecycle Management: Master model registries, staging, promotion, and lineage.

• Deployment Recipes: Real-time vs. batch, Docker, Kubernetes, FastAPI—plus blue-green, rolling, and rollback techniques.

• Monitoring & Alerting: Keep production stable with actionable metrics, drift detection, and alert systems.

• Cost & Resource Optimization: Tame your compute, storage, and budget before they tame you.

• Security & Compliance: Practical approaches to pipeline security, auditability, and governance.

• Case Studies: CI/CD in finance, retail, healthcare, and lessons from industry giants.

• Building Your Platform: How to scale from scrappy scripts to organization-wide, automated MLOps.

If you want to stop firefighting and start delivering robust, fault-tolerant, and explainable machine learning—this is your field guide.

Ready to automate, monitor, and scale every model you deploy? Turn the page. Production awaits.