Practical guide mlops data von gabe avis (6 Ergebnisse)
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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Zustand: new. Paperback. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
