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5 books on MLOps [PDF]
October 24, 2024 | 21 |
Books on MLOps (Machine Learning Operations) describe essential principles and best practices required to efficiently develop, deploy and manage AI models.
1. Implementing MLOps in the Enterprise
2023 by Yaron Haviv, Noah Gift
"In 'Implementing MLOps in the Enterprise,' businesses are guided through the essential steps of establishing operational machine learning pipelines to meet the growing demand for scalability and real-time access. This practical handbook is designed to help senior data scientists, MLOps engineers and machine learning engineers overcome common challenges hindering the deployment of ML models into production. Authors Yaron Haviv and Noah Gift advocate a production-first approach, emphasizing the design of continuous operational pipelines that automate and streamline processes to ensure scalability and repeatability. By focusing on automating components and ensuring rapid deployment, readers will learn how to deliver immediate business value while adapting to dynamic MLOps requirements. The book covers essential topics such as understanding the MLOps process, building effective pipelines, scaling MLOps across organizations, exploring use cases and preparing for future advancements in MLOps, including hybrid deployments and real-time predictions. Additionally, it provides insights into leveraging pre-trained models like HuggingFace and OpenAI to enhance MLOps strategies."
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2. 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
3. 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
4. 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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5. 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. Implementing MLOps in the Enterprise
2023 by Yaron Haviv, Noah Gift
"In 'Implementing MLOps in the Enterprise,' businesses are guided through the essential steps of establishing operational machine learning pipelines to meet the growing demand for scalability and real-time access. This practical handbook is designed to help senior data scientists, MLOps engineers and machine learning engineers overcome common challenges hindering the deployment of ML models into production. Authors Yaron Haviv and Noah Gift advocate a production-first approach, emphasizing the design of continuous operational pipelines that automate and streamline processes to ensure scalability and repeatability. By focusing on automating components and ensuring rapid deployment, readers will learn how to deliver immediate business value while adapting to dynamic MLOps requirements. The book covers essential topics such as understanding the MLOps process, building effective pipelines, scaling MLOps across organizations, exploring use cases and preparing for future advancements in MLOps, including hybrid deployments and real-time predictions. Additionally, it provides insights into leveraging pre-trained models like HuggingFace and OpenAI to enhance MLOps strategies."
Download PDF
2. 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
3. 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
4. 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
5. 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
