Isbn: 9798289408556 - ai engineering blueprint for beginners: a practical guide to designing, deploying, and maintaining scalable ai systems with modern tools and mlops (digital skills mastery) (13 resultados)

Condición: New.

Paperback. Condición: New.

Condición: As New. Unread book in perfect condition.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: New.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: As New. Unread book in perfect condition.

Paperback. Condición: New.

Condición: New. Print on Demand.

Paperback. Condición: New.

Paperback. Condición: new. Paperback. Build Reliable, Scalable, and Ethical AI Systems-From the Ground UpAre you ready to move beyond AI theory and start building practical, production-ready solutions?Whether you're a beginner looking for a roadmap or a developer stepping into the world of AI engineering, this guide provides everything you need to succeed in real-world AI projects.This isn't just another book about machine learning models. It's a step-by-step blueprint for designing, deploying, monitoring, and maintaining AI systems that solve real problems and continue performing in dynamic environments.What You'll Learn: Design AI systems with real-world impactDefine clear goals, evaluate trade-offs, and select the right architectures based on your needs.Build clean, well-managed data pipelinesLearn how to gather, prepare, and version your datasets for reproducibility and scale.Master model versioning, retraining, and automationKeep your models fresh and your pipelines efficient using tools like MLflow, DVC, and Airflow.Monitor drift and track performance with confidenceSet up metrics, alerts, and dashboards to detect issues before they impact users.Secure your AI workflows and ensure complianceProtect sensitive data, understand regulations like GDPR and HIPAA, and build with trust and transparency.Integrate human feedback and build ethical AIDesign feedback loops and human-in-the-loop systems that improve outcomes and reduce bias.What's Inside: Real-world case studies and applied best practicesTools and libraries cheat sheet (PyTorch, TensorFlow, FastAPI, etc.)Glossary of key AI engineering termsModel evaluation matrix and deployment checklistsA complete appendix packed with resources, templates, and guidesWho This Book Is For: Aspiring AI and ML engineersSoftware developers transitioning into AI rolesTechnical team leads and project managers overseeing AI initiativesAnyone who wants to understand how to responsibly build and operate modern AI systemsThis book focuses on production-grade AI engineering. You'll gain actionable knowledge you can use right away-whether you're working on your first AI project or scaling an enterprise-level system. Grab your copy today and start building AI systems that are accurate, explainable, and built to last. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: new. Paperback. Build Reliable, Scalable, and Ethical AI Systems-From the Ground UpAre you ready to move beyond AI theory and start building practical, production-ready solutions?Whether you're a beginner looking for a roadmap or a developer stepping into the world of AI engineering, this guide provides everything you need to succeed in real-world AI projects.This isn't just another book about machine learning models. It's a step-by-step blueprint for designing, deploying, monitoring, and maintaining AI systems that solve real problems and continue performing in dynamic environments.What You'll Learn: Design AI systems with real-world impactDefine clear goals, evaluate trade-offs, and select the right architectures based on your needs.Build clean, well-managed data pipelinesLearn how to gather, prepare, and version your datasets for reproducibility and scale.Master model versioning, retraining, and automationKeep your models fresh and your pipelines efficient using tools like MLflow, DVC, and Airflow.Monitor drift and track performance with confidenceSet up metrics, alerts, and dashboards to detect issues before they impact users.Secure your AI workflows and ensure complianceProtect sensitive data, understand regulations like GDPR and HIPAA, and build with trust and transparency.Integrate human feedback and build ethical AIDesign feedback loops and human-in-the-loop systems that improve outcomes and reduce bias.What's Inside: Real-world case studies and applied best practicesTools and libraries cheat sheet (PyTorch, TensorFlow, FastAPI, etc.)Glossary of key AI engineering termsModel evaluation matrix and deployment checklistsA complete appendix packed with resources, templates, and guidesWho This Book Is For: Aspiring AI and ML engineersSoftware developers transitioning into AI rolesTechnical team leads and project managers overseeing AI initiativesAnyone who wants to understand how to responsibly build and operate modern AI systemsThis book focuses on production-grade AI engineering. You'll gain actionable knowledge you can use right away-whether you're working on your first AI project or scaling an enterprise-level system. Grab your copy today and start building AI systems that are accurate, explainable, and built to last. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…