Ultimate MLOps for Machine Learning Models
Use Real Case Studies to Efficiently Build, Deploy, and Scale Machine Learning Pipelines with MLOps (English Edition)
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Ultimate MLOps for Machine Learning Models
Use Real Case Studies to Efficiently Build, Deploy, and Scale Machine Learning Pipelines with MLOps (English Edition)
About this book
The only MLOps guide you'll ever need
Book DescriptionThis book is an essential resource for professionals aiming to streamline and optimize their machine learning operations. This comprehensive guide provides a thorough understanding of the MLOps life cycle, from model development and training to deployment and monitoring. By delving into the intricacies of each phase, the book equips readers with the knowledge and tools needed to create robust, scalable, and efficient machine learning workflows.
Key chapters include a deep dive into essential MLOps tools and technologies, effective data pipeline management, and advanced model optimization techniques. The book also addresses critical aspects such as scalability challenges, data and model governance, and security in machine learning operations. Each topic is presented with practical insights and real-world case studies, enabling readers to apply best practices in their job roles.
Table of Contents1. Introduction to MLOps2. Understanding Machine Learning Lifecycle3. Essential Tools and Technologies in MLOps4. Data Pipelines and Management in MLOps5. Model Development and Training6. Model Optimization Techniques for Performance7. Efficient Model Deployment and Monitoring Strategies8. Scalability Challenges and Solutions in MLOps9. Data, Model Governance, and Compliance in Production Environments10. Security in Machine Learning Operations11. Case Studies and Future Trends in MLOps Index
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Information
Table of contents
- Cover Page
- Title Page
- Copyright Page
- Dedication Page
- About the Author
- About the Technical Reviewer
- Acknowledgements
- Preface
- Get a Free eBook
- Errata
- Table of Contents
- 1. Introduction to MLOps
- 2. Understanding Machine Learning Lifecycle
- 3. Essential Tools and Technologies in MLOps
- 4. Data Pipelines and Management in MLOps
- 5. Model Development and Training
- 6. Model Optimization Techniques for Performance
- 7. Efficient Model Deployment and Monitoring Strategies
- 8. Scalability Challenges and Solutions in MLOps
- 9. Data, Model Governance, and Compliance in Production Environments
- 10. Security in Machine Learning Operations
- 11. Case Studies and Future Trends in MLOps
- Index