Название: Mastering MLOps Architecture: From Code to Deployment: Manage the production cycle of continual learning ML models with MLOpsАвтор: Raman Jhajj
Издательство: BPB Publications
Год: 2024
Страниц: 286
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB
Harness the power of MLOps for managing real time Machine Learning project cycle.
MLOps is the intersection of DevOps, data engineering and Machine Learning. Working in the field of machine learning is highly dependent on ever-changing data, whereas MLOps is needed to deliver excellent ML and AI results. This book provides a practical guide to MLOps for data scientists, data engineers, and other professionals involved in building and deploying Machine Learning systems. It introduces MLOps, explaining its core concepts like continuous integration and delivery for Machine Learning. It outlines MLOps components and architecture, providing an understanding of how MLOps supports robust ML systems that continuously improve.
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. With its comprehensive coverage and practical focus, this book enables data scientists, data engineers, DevOps engineers, and technical leaders to effectively leverage MLOps. Readers can gain skills to build efficient CI/CD pipelines, deploy models faster, and make their ML systems more reliable 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.
- Monitor and maintain models in production to detect degradation.
- Create automated workflows for retraining and updating models in production.
Who this book is for:
Machine 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.
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