Metaflow For Data Science Workflows
Download Metaflow For Data Science Workflows PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Metaflow For Data Science Workflows book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Metaflow for Data Science Workflows
"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
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
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