Ultimate MLOps for Machine Learning Models: Use Real Case Studies to Efficiently Build, Deploy, and Scale Machine Learning Pipelines with MLOps

Dorle, Saurabh D.; AVA, Orange

ISBN 10: 8197651205 / ISBN 13: 9788197651205

Edité par Orange Education Pvt. Ltd, 2024

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Détails bibliographiques

• Titre: Ultimate MLOps for Machine Learning Models: Use Real Case Studies to Efficiently Build, Deploy, and Scale Machine Learning Pipelines with MLOps
• Éditeur: Orange Education Pvt. Ltd
• Date de parution: 2024
• Langue: anglais
• ISBN à 10 chiffres: 8197651205
• ISBN à 13 chiffres: 9788197651205
• Reliure: Couverture souple
• État de l'article: New

A propos de ce titre

Synopsis

The only MLOps guide you'll ever need

Unlock the secrets to streamlined MLOps for scalable machine learning solutions in today’s data-driven world. This book is your step-by-step practical guide to machine learning lifecycle management—covering everything from deploying and monitoring machine learning models in production to optimizing data pipelines for real results.

Book Description
This 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.

What You’ll Learn Inside:

• End-to-end methods for building machine learning pipelines with MLOps, including data pipeline management in MLOps workflows
• Actionable strategies and real-world case studies for efficient machine learning operations at scale
• Comprehensive model optimization techniques, model monitoring strategies, and best practices for live deployment
• Tips on secure deployment and monitoring for ML models, along with machine learning governance and compliance in production
• Insights on MLOps lifecycle management, scalability challenges, and solutions to future-proof your models
• Coverage of advanced MLOps tools and technologies explained through case studies MLOps and practical walkthroughs
• Guidance for data pipeline workflow, model retraining, risk management, and ML governance compliance

Why This Book?

• Accessible to all backgrounds, offering MLOps best practices and workflow strategies for rapid success
• Clear, simple advice for machine learning scalability, operational security, and compliance needs
• Perfect for professionals or students seeking hands-on mastery in modern machine learning MLOps

Who Will Benefit?

• Data scientists, engineers, and managers wanting to enhance ML model deployment, monitoring, and optimization
• Teams seeking to scale with scalable machine learning solutions and secure operations
• Anyone aiming to understand genuine real-world machine learning and MLOps scalability

Start building, deploying, and optimizing machine learning models with confidence!

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