Metaflow For Data Science Workflows

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Metaflow For Data Science Workflows

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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-13

Metaflow For Data Science Workflows written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-13 with Computers categories.

"Metaflow for Data Science Workflows" "Metaflow for Data Science Workflows" is an authoritative guide to building, managing, and scaling modern data science workflows using the Metaflow framework. This comprehensive book opens with a critical analysis of the evolution of data science pipelines, examining the challenges of reproducibility, scalability, and complexity that confront today’s practitioners. Readers are introduced to the transformative potential of orchestration tools within MLOps and DataOps, placing Metaflow in context through in-depth comparisons with Airflow and Kubeflow, while establishing a strong foundation in core concepts such as Flows, Steps, Artifacts, and the Directed Acyclic Graph (DAG) paradigm. Spanning Metaflow’s robust architecture and its integration with cloud and enterprise environments, the book delves into technical mechanisms essential for workflow composition, dynamic branching, parallel execution, and advanced artifact management. It empowers readers to develop resilient, production-ready data pipelines through best practices in parameterization, modular step design, error handling, and collaboration. Extensive attention is given to scalable deployment strategies—from local testing to distributed cloud execution on AWS, Kubernetes, and serverless platforms—and to maintaining fault tolerance, cost efficiency, and regulatory compliance at enterprise scale. The discussion extends beyond theory with practical guidance on experiment management, CI/CD integration, and operational monitoring, ensuring reproducibility and traceability through versioning, tagging, and comprehensive audit trails. Real-world case studies, patterns for hybrid and multi-cloud orchestration, and insights into emerging trends position this book as an indispensable resource for data scientists, engineers, and technical leaders seeking to implement robust and future-proof data science workflows with Metaflow.

Effective Data Science Infrastructure

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Author : Ville Tuulos
language : en
Publisher: Simon and Schuster
Release Date : 2022-08-16

Effective Data Science Infrastructure written by Ville Tuulos and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-16 with Computers categories.

Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company's specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems.

Effective Data Science Infrastructure

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Author : Ville Tuulos
language : en
Publisher: Simon and Schuster
Release Date : 2022-08-30

Effective Data Science Infrastructure written by Ville Tuulos and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-30 with Computers categories.

Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Table of Contents 1 Introducing data science infrastructure 2 The toolchain of data science 3 Introducing Metaflow 4 Scaling with the compute layer 5 Practicing scalability and performance 6 Going to production 7 Processing data 8 Using and operating models 9 Machine learning with the full stack

Designing Machine Learning Systems

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Author : Chip Huyen
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-05-17

Designing Machine Learning Systems written by Chip Huyen and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-17 with Computers categories.

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision--such as how to process and create training data, which features to use, how often to retrain models, and what to monitor--in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as: Engineering data and choosing the right metrics to solve a business problem Automating the process for continually developing, evaluating, deploying, and updating models Developing a monitoring system to quickly detect and address issues your models might encounter in production Architecting an ML platform that serves across use cases Developing responsible ML systems

Designing Deep Learning Systems

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Author : Chi Wang
language : en
Publisher: Simon and Schuster
Release Date : 2023-09-19

Designing Deep Learning Systems written by Chi Wang and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-19 with Computers categories.

A vital guide to building the platforms and systems that bring deep learning models to production. In Designing Deep Learning Systems you will learn how to: Transfer your software development skills to deep learning systems Recognize and solve common engineering challenges for deep learning systems Understand the deep learning development cycle Automate training for models in TensorFlow and PyTorch Optimize dataset management, training, model serving and hyperparameter tuning Pick the right open-source project for your platform Deep learning systems are the components and infrastructure essential to supporting a deep learning model in a production environment. Written especially for software engineers with minimal knowledge of deep learning’s design requirements, Designing Deep Learning Systems is full of hands-on examples that will help you transfer your software development skills to creating these deep learning platforms. You’ll learn how to build automated and scalable services for core tasks like dataset management, model training/serving, and hyperparameter tuning. This book is the perfect way to step into an exciting—and lucrative—career as a deep learning engineer. About the technology To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. Th is book gives you that depth. About the book Designing Deep Learning Systems: A software engineer's guide teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer’s perspective, including its major components and how they are connected. Then, it carefully guides you through the engineering methods you’ll need to build your own maintainable, efficient, and scalable deep learning platforms. What's inside The deep learning development cycle Automate training in TensorFlow and PyTorch Dataset management, model serving, and hyperparameter tuning A hands-on deep learning lab About the reader For software developers and engineering-minded data scientists. Examples in Java and Python. About the author Chi Wang is a principal software developer in the Salesforce Einstein group. Donald Szeto was the co-founder and CTO of PredictionIO. Table of Contents 1 An introduction to deep learning systems 2 Dataset management service 3 Model training service 4 Distributed training 5 Hyperparameter optimization service 6 Model serving design 7 Model serving in practice 8 Metadata and artifact store 9 Workflow orchestration 10 Path to production

Encyclopedia Of Data Science And Machine Learning

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Author : Wang, John
language : en
Publisher: IGI Global
Release Date : 2023-01-20

Encyclopedia Of Data Science And Machine Learning written by Wang, John and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-20 with Computers categories.

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

Adversarial Ai Attacks Mitigations And Defense Strategies

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Author : John Sotiropoulos
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-26

Adversarial Ai Attacks Mitigations And Defense Strategies written by John Sotiropoulos and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-26 with Computers categories.

“The book not only explains how adversarial attacks work but also shows you how to build your own test environment and run attacks to see how they can corrupt ML models. It's a comprehensive guide that walks you through the technical details and then flips to show you how to defend against these very same attacks.” – Elaine Doyle, VP and Cybersecurity Architect, Salesforce Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Understand the unique security challenges presented by predictive and generative AI Explore common adversarial attack strategies as well as emerging threats such as prompt injection Mitigate the risks of attack on your AI system with threat modeling and secure-by-design methods Book DescriptionAdversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips you with the skills to secure AI technologies, moving beyond research hype or business-as-usual activities. Learn how to defend AI and LLM systems against manipulation and intrusion through adversarial attacks such as poisoning, trojan horses, and model extraction, leveraging DevSecOps, MLOps, and other methods to secure systems. This strategy-based book is a comprehensive guide to AI security, combining structured frameworks with practical examples to help you identify and counter adversarial attacks. Part 1 introduces the foundations of AI and adversarial attacks. Parts 2, 3, and 4 cover key attack types, showing how each is performed and how to defend against them. Part 5 presents secure-by-design AI strategies, including threat modeling, MLSecOps, and guidance aligned with OWASP and NIST. The book concludes with a blueprint for maturing enterprise AI security based on NIST pillars, addressing ethics and safety under Trustworthy AI. By the end of this book, you’ll be able to develop, deploy, and secure AI systems against the threat of adversarial attacks effectively.What you will learn Set up a playground to explore how adversarial attacks work Discover how AI models can be poisoned and what you can do to prevent this Learn about the use of trojan horses to tamper with and reprogram models Understand supply chain risks Examine how your models or data can be stolen in privacy attacks See how GANs are weaponized for Deepfake creation and cyberattacks Explore emerging LLM-specific attacks, such as prompt injection Leverage DevSecOps, MLOps and MLSecOps to secure your AI system Who this book is for This book tackles AI security from both angles - offense and defence. AI developers and engineers will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats to AI and mitigate the risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you’ll need a basic understanding of security, ML concepts, and Python.

Product Mastery A Masterclass In Product Management

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Author : Abhishek Kumar Agarwal
language : en
Publisher: Abhishek Kumar Agarwal
Release Date : 2023-12-19

Product Mastery A Masterclass In Product Management written by Abhishek Kumar Agarwal and has been published by Abhishek Kumar Agarwal this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-19 with Young Adult Nonfiction categories.

Are you ready to revolutionize your product management skills in the era of AI and ML? Step into a world where the role of a product manager has evolved, redefined by the insights of Abhishek Agarwal. Abhishek's remarkable journey, spanning from his roots in rural India to key leadership positions in Fortune 500 companies, serves as the backdrop for this transformative book. "PRODUCT MASTERY - A MASTERCLASS IN PRODUCT MANAGEMENT" offers a pragmatic and empowering approach to thriving in the ever-evolving domain of product management, particularly in the age of Artificial Intelligence (AI) and Machine Learning (ML). Abhishek Agarwal's credentials are impeccable, underscored by his recognition with the ET Inspiring Leaders - Global Icon in the field of Machine Learning & Artificial Intelligence, and Indian Achievers' Award 2023. His journey is a testament to his unwavering commitment to technology accessibility and his belief in the transformative power of AI and ML. With experience at prominent organizations like Unilever, Amazon, and Hewlett Packard Enterprise, Abhishek's strategic insight have set him on a path to become a true visionary in product management. In "PRODUCT MASTERY - A MASTERCLASS IN PRODUCT MANAGEMENT," Abhishek demystifies AI in practical terms, presenting a clear and logical framework. He provides accessible definitions, abundant insights, and expert guidance, making complex concepts understandable to all. Here are some key highlights: The Evolution of Product Management: Abhishek delves into the transformation of the product management landscape. He explores how the role has shifted from traditional practices to a customer-centric approach, emphasizing the importance of solving customer problems. Lean startup methodologies and cross-functional collaboration have changed the game, as product managers forecast product success with accuracy before they are built. The book captures the essence of these significant shifts and sets the stage for the role's future evolution. AI and ML Integration: AI and ML are no longer on the fringes; they have become integral to product management. Abhishek delves deep into how these technologies reshape industries, driving innovation and enhancing user experiences. He doesn't stop at the advantages; he also highlights the crucial role of ethics in the context of generative AI, emphasizing the importance of responsible development and deployment. Practical Guidance: The book offers a comprehensive guide, drawing from Abhishek's experience at Amazon Web Services (AWS), where he played a pivotal role in the development of SageMaker products. It covers everything from the inception of ideas to product launches, blending theory with real-world insights to provide practical guidance. "PRODUCT MASTERY - A MASTERCLASS IN PRODUCT MANAGEMENT" is not just a book; it's an empowering compass that will guide you through the intricacies of modern product management. Abhishek's vision, expertise, and commitment to ethical considerations in AI and ML make this book more than just informative; it's a transformational resource. Whether you're an aspiring product manager, a seasoned professional, or an entrepreneurial spirit, this book equips you with the tools, frameworks, and wisdom to excel in the ever-evolving landscape of innovation and strategy. As you dive into the pages of this book, you'll find not only valuable insights but also a reflection of your own aspirations and experiences. Join Abhishek on this journey, and explore a path filled with discovery, revelation, and growth—a path with the potential to reshape industries and inspire minds. Don't miss this opportunity to navigate the complexities of AI and ML with the wisdom of a visionary. Maximize your success and embark on a thrilling journey today.

Data Pipelines With Apache Airflow

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Author : Bas P. Harenslak
language : en
Publisher: Simon and Schuster
Release Date : 2021-04-27

Data Pipelines With Apache Airflow written by Bas P. Harenslak and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with Computers categories.

For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills"--Back cover.

Building Machine Learning Pipelines

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Author : Hannes Hapke
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2020-07-13

Building Machine Learning Pipelines written by Hannes Hapke and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-13 with Computers categories.

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques