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SKU/Artículo: AMZ-9355519494
Mastering MLOps Architecture: From Code to Deployment: Manage the production cycle of continual learning ML models with MLOps (English Edition)
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.56 kg
0.56 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Harness the power of MLOps for managing real time machine learning project cycleKey Features● Comprehensive coverage of MLOps concepts, architecture, tools and techniques.● Practical focus on building end-to-end ML Systems for Continual Learning with MLOps.● Actionable insights on CI/CD, monitoring, continual model training and automated retraining.DescriptionMLOps, a combination of DevOps, data engineering, and machine learning, is crucial for delivering high-quality machine learning results due to the dynamic nature of machine learning data. This book delves into MLOps, covering its core concepts, components, and architecture, demonstrating how MLOps fosters robust and continuously improving machine learning systems.By covering the end-to-end machine learning pipeline from data to deployment, the book helps readers implement MLOps workflows. It discusses techniques like feature engineering, model development, A/B testing, and canary deployments. The book equips readers with knowledge of MLOps tools and infrastructure for tasks like model tracking, model governance, metadata management, and pipeline orchestration. Monitoring and maintenance processes to detect model degradation are covered in depth. Readers can gain skills to build efficient CI/CD pipelines, deploy models faster, and make their ML systems more reliable, robust and production-ready.Overall, the book is an indispensable guide to MLOps and its applications for delivering business value through continuous machine learning and AI.What you will learn● Architect robust MLOps infrastructure with components like feature stores.● Leverage MLOps tools like model registries, metadata stores, pipelines.● Build CI/CD workflows to deploy models faster and continually.● Monitor and maintain models in production to detect degradation.● Create automated workflows for retraining and updating models in production.Who this book is forMachine learning specialists, data scientists, DevOps professionals, software development teams, and all those who want to adopt the DevOps approach in their agile machine learning experiments and applications. Prior knowledge of machine learning and Python programming is desired.Table of Contents1. Getting Started with MLOps2. MLOps Architecture and Components3. MLOps Infrastructure and Tools4. What are Machine Learning Systems?5. Data Preparation and Model Development6. Model Deployment and Serving7. Continuous Delivery of Machine Learning Models8. Continual Learning9. Continuous Monitoring, Logging, and Maintenance
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