{"product_id":"effective-data-science-infrastructure-how-to-make-data-scientists-productive-9781617299193","title":"Effective Data Science Infrastructure: How to Make Data Scientists Productive","description":"\u003cb\u003eSimplify data science infrastructure to give data scientists an efficient path from prototype to production.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eEffective Data Science Infrastructure\u003c\/i\u003e you will learn how to: \u003cp\u003e\u003c\/p\u003e Design data science infrastructure that boosts productivity\u003cbr\u003e Handle compute and orchestration in the cloud\u003cbr\u003e Deploy machine learning to production\u003cbr\u003e Monitor and manage performance and results\u003cbr\u003e Combine cloud-based tools into a cohesive data science environment\u003cbr\u003e Develop reproducible data science projects using Metaflow, Conda, and Docker\u003cbr\u003e Architect complex applications for multiple teams and large datasets\u003cbr\u003e Customize and grow data science infrastructure \u003cp\u003e\u003c\/p\u003e \u003ci\u003eEffective Data Science Infrastructure: How to make data scientists more productive\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003eThe author is donating proceeds from this book to charities that support women and underrepresented groups in data science. \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e About the technology\u003cbr\u003e 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. \u003cp\u003e\u003c\/p\u003e About the book\u003cbr\u003e \u003ci\u003eEffective Data Science Infrastructure\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003e What's inside \u003cp\u003e\u003c\/p\u003e Handle compute and orchestration in the cloud\u003cbr\u003e Combine cloud-based tools into a cohesive data science environment\u003cbr\u003e Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem\u003cbr\u003e Architect complex applications that require large datasets and models, and a team of data scientists \u003cp\u003e\u003c\/p\u003eAbout the reader\u003cbr\u003e For infrastructure engineers and engineering-minded data scientists who are familiar with Python. \u003cp\u003e\u003c\/p\u003e About the author\u003cbr\u003e At Netflix, \u003cb\u003eVille Tuulos\u003c\/b\u003e designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. \u003cp\u003e\u003c\/p\u003e Table of Contents\u003cbr\u003e 1 Introducing data science infrastructure\u003cbr\u003e 2 The toolchain of data science\u003cbr\u003e 3 Introducing Metaflow\u003cbr\u003e 4 Scaling with the compute layer\u003cbr\u003e 5 Practicing scalability and performance\u003cbr\u003e 6 Going to production\u003cbr\u003e 7 Processing data\u003cbr\u003e 8 Using and operating models\u003cbr\u003e 9 Machine learning with the full stack\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cstrong\u003eVille Tuulos\u003c\/strong\u003e has been developing tools and infrastructure for data science and machine learning for over two decades. At Netflix, he designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure.\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":50450347622674,"sku":"9781617299193","price":55.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_237658d4-4fc5-4129-90e9-4dd346a9b2a3.jpg?v=1729754549","url":"https:\/\/surprise-castle.myshopify.com\/products\/effective-data-science-infrastructure-how-to-make-data-scientists-productive-9781617299193","provider":"Surprise Castle","version":"1.0","type":"link"}