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4 books on MLOps [PDF]
Updated: February 25, 2024 | 20 |
Books on MLOps (Machine Learning Operations) are invaluable assets for startups specializing in MLOps and AIOps (Artificial Intelligence Operations) as they delve into the essential principles and best practices required to efficiently develop, deploy, and manage machine learning models at scale. These texts provide startups with insights into the automation of the machine learning lifecycle, model version control, continuous integration, and monitoring, which are crucial aspects of building robust and sustainable AI systems.
1. Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale
2021 by Emmanuel Raj
In "Engineering MLOps," you'll gain comprehensive insights into the world of MLOps, enriched with practical Azure-based examples. This book equips you with the knowledge and skills to develop programs, create robust and scalable machine learning (ML) models, and construct ML pipelines tailored for secure production deployments. Starting with an introduction to the MLOps workflow, you'll delve into the art of writing programs for ML model training. You'll then explore serialization and packaging methods for ML models post-training, essential for streamlined deployments and model interoperability. As you progress, you'll master the art of building ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitoring pipelines, ensuring a systematic approach to building, deploying, monitoring, and governing ML solutions across various industries. Real-world projects will serve as your testing ground, allowing you to apply your newfound knowledge effectively. By the book's conclusion, you'll possess a holistic understanding of MLOps and be primed to implement these principles in your organization. This resource is ideal for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders aiming to navigate the intricate landscape of building, deploying, and maintaining ML systems using MLOps techniques, assuming a foundational knowledge of machine learning as a starting point.
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2. Practical MLOps
2021 by Noah Gift, Alfredo Deza
In "Practical MLOps," you'll embark on a journey to understand the essence of MLOps, distinguishing it from DevOps, and gain the practical know-how to implement it effectively for the operationalization of your machine learning models. Whether you're a current or aspiring machine learning engineer, or simply have a background in data science and Python, this book equips you with a solid foundation in MLOps tools and techniques, encompassing AutoML, monitoring, and logging. Delving into major cloud platforms like AWS, Microsoft Azure, and Google Cloud, you'll expedite the delivery of functional machine learning systems, allowing you to direct your focus towards solving critical business challenges. This guide will empower you to apply DevOps principles to the realm of machine learning, construct production-ready machine learning systems, and ensure their continuous upkeep. You'll also learn how to effectively monitor, instrument, load-test, and operationalize these systems. Armed with the knowledge from this book, you'll be well-prepared to select the appropriate MLOps tools for specific machine learning tasks and successfully run machine learning models across various platforms and devices, including mobile phones and specialized hardware. "Practical MLOps" provides a valuable head start on your journey to harness the power of MLOps, helping you deliver efficient machine learning solutions that drive your business forward.
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3. Accelerated DevOps with AI, ML & RPA: Non-Programmer’s Guide to AIOPS & MLOPS
2020 by Stephen Fleming
This book serves as a guide to AIOPS and MLOPS tailored for non-programmers. Regardless of your role, understanding AIOPS and MLOPS can be beneficial. Whether you're a Business Consultant seeking to enhance system efficiency and profitability through automation, a Technology Consultant aiming for greater agility and automation in operations, a Technology Professional exploring new career opportunities in these emerging domains, or an HR or Training professional keen on understanding job and training requirements for these roles, this book provides valuable insights. It offers an insider's perspective on how AI is poised to revolutionize software development and deployment in the coming years, shedding light on the fast-paced evolution of this field, even for those who witnessed the world before the internet became a part of our daily lives. "Accelerated DevOps with AI, ML & RPA" is your source for staying up-to-date with the unfolding narrative of AIOPS and MLOPS.
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4. Introducing MLOps
2020 by Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann
In "Introducing MLOps," you'll gain a comprehensive understanding of the fundamental concepts of MLOps, equipping data scientists and application engineers to not only operationalize ML models effectively but also to continuously enhance and maintain them for long-term success. Drawing insights from real-world MLOps applications across the globe, a team of nine machine learning experts guides you through the five crucial stages of the model life cycle: Build, Preproduction, Deployment, Monitoring, and Governance. This invaluable resource reveals how robust MLOps practices can be seamlessly integrated into each phase of the process. With this book, you'll streamline ML pipelines and workflows to unlock the full potential of your data science endeavors, ensuring that your ML models remain accurate and relevant over time. Additionally, you'll discover how to design an MLOps life cycle that minimizes organizational risks, emphasizing fairness, transparency, and explainability in your models. Whether you're deploying ML models within your pipelines or integrating them into complex, non-standardized business systems, this book provides the insights and strategies you need to thrive in the world of MLOps. It's an essential guide for those seeking to optimize ML operations, reduce friction in their workflows, and harness the full value of their data science efforts.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale
2021 by Emmanuel Raj
In "Engineering MLOps," you'll gain comprehensive insights into the world of MLOps, enriched with practical Azure-based examples. This book equips you with the knowledge and skills to develop programs, create robust and scalable machine learning (ML) models, and construct ML pipelines tailored for secure production deployments. Starting with an introduction to the MLOps workflow, you'll delve into the art of writing programs for ML model training. You'll then explore serialization and packaging methods for ML models post-training, essential for streamlined deployments and model interoperability. As you progress, you'll master the art of building ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitoring pipelines, ensuring a systematic approach to building, deploying, monitoring, and governing ML solutions across various industries. Real-world projects will serve as your testing ground, allowing you to apply your newfound knowledge effectively. By the book's conclusion, you'll possess a holistic understanding of MLOps and be primed to implement these principles in your organization. This resource is ideal for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders aiming to navigate the intricate landscape of building, deploying, and maintaining ML systems using MLOps techniques, assuming a foundational knowledge of machine learning as a starting point.
Download PDF
2. Practical MLOps
2021 by Noah Gift, Alfredo Deza
In "Practical MLOps," you'll embark on a journey to understand the essence of MLOps, distinguishing it from DevOps, and gain the practical know-how to implement it effectively for the operationalization of your machine learning models. Whether you're a current or aspiring machine learning engineer, or simply have a background in data science and Python, this book equips you with a solid foundation in MLOps tools and techniques, encompassing AutoML, monitoring, and logging. Delving into major cloud platforms like AWS, Microsoft Azure, and Google Cloud, you'll expedite the delivery of functional machine learning systems, allowing you to direct your focus towards solving critical business challenges. This guide will empower you to apply DevOps principles to the realm of machine learning, construct production-ready machine learning systems, and ensure their continuous upkeep. You'll also learn how to effectively monitor, instrument, load-test, and operationalize these systems. Armed with the knowledge from this book, you'll be well-prepared to select the appropriate MLOps tools for specific machine learning tasks and successfully run machine learning models across various platforms and devices, including mobile phones and specialized hardware. "Practical MLOps" provides a valuable head start on your journey to harness the power of MLOps, helping you deliver efficient machine learning solutions that drive your business forward.
Download PDF
3. Accelerated DevOps with AI, ML & RPA: Non-Programmer’s Guide to AIOPS & MLOPS
2020 by Stephen Fleming
This book serves as a guide to AIOPS and MLOPS tailored for non-programmers. Regardless of your role, understanding AIOPS and MLOPS can be beneficial. Whether you're a Business Consultant seeking to enhance system efficiency and profitability through automation, a Technology Consultant aiming for greater agility and automation in operations, a Technology Professional exploring new career opportunities in these emerging domains, or an HR or Training professional keen on understanding job and training requirements for these roles, this book provides valuable insights. It offers an insider's perspective on how AI is poised to revolutionize software development and deployment in the coming years, shedding light on the fast-paced evolution of this field, even for those who witnessed the world before the internet became a part of our daily lives. "Accelerated DevOps with AI, ML & RPA" is your source for staying up-to-date with the unfolding narrative of AIOPS and MLOPS.
Download PDF
4. Introducing MLOps
2020 by Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann
In "Introducing MLOps," you'll gain a comprehensive understanding of the fundamental concepts of MLOps, equipping data scientists and application engineers to not only operationalize ML models effectively but also to continuously enhance and maintain them for long-term success. Drawing insights from real-world MLOps applications across the globe, a team of nine machine learning experts guides you through the five crucial stages of the model life cycle: Build, Preproduction, Deployment, Monitoring, and Governance. This invaluable resource reveals how robust MLOps practices can be seamlessly integrated into each phase of the process. With this book, you'll streamline ML pipelines and workflows to unlock the full potential of your data science endeavors, ensuring that your ML models remain accurate and relevant over time. Additionally, you'll discover how to design an MLOps life cycle that minimizes organizational risks, emphasizing fairness, transparency, and explainability in your models. Whether you're deploying ML models within your pipelines or integrating them into complex, non-standardized business systems, this book provides the insights and strategies you need to thrive in the world of MLOps. It's an essential guide for those seeking to optimize ML operations, reduce friction in their workflows, and harness the full value of their data science efforts.
Download PDF
How to download PDF:
1. Install Google Books Downloader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
