Artículo: AMZ-B0FKZBJQWX

The Practical Guide to MLOps & Data Pipeline Automation: Deliver Fast, Fault-Tolerant Machine Learning with CI/CD, Model Monitoring, and Rollback Strategies

Disponibilidad
En stock

Peso con empaque
0.51 kg

Devolución
Sí

Condición
Nuevo

Producto de
Amazon

Sobre este producto

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.