Description: MLOps Engineering at Scale by Carl Osipov Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML systems infrastructure. Following a real-world use case for calculating taxi fares, youll learn how to get a serverless ML pipeline up and running using AWS services. Clear and detailed tutorials show you how to develop reliable, flexible, and scalable machine learning systems without time-consuming management tasks or the costly overheads of physical hardware. about the technologyYour new machine learning model is ready to put into production, and suddenly all your time is taken up by setting up your server infrastructure. Serverless machine learning offers a productivity-boosting alternative. It eliminates the time-consuming operations tasks from your machine learning lifecycle, letting out-of-the-box cloud services take over launching, running, and managing your ML systems. With the serverless capabilities of major cloud vendors handling your infrastructure, youre free to focus on tuning and improving your models. about the book Cloud Native Machine Learning is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. Youll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, youll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, youll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform. When youre done, youll have the tools to easily bridge the gap between ML models and a fully functioning production system. whats inside Extracting, transforming, and loading datasetsQuerying datasets with SQLUnderstanding automatic differentiation in PyTorchDeploying trained models and pipelines as a service endpointMonitoring and managing your pipelines life cycleMeasuring performance improvements about the readerFor data professionals with intermediate Python skills and basic familiarity with machine learning. No cloud experience required. about the author Carl Osipov has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the worlds foremost experts in machine learning and also helped manage the companys efforts to democratize artificial intelligence. You can learn more about Carl from his blog Clouds With Carl. FORMAT Paperback LANGUAGE English CONDITION Brand New Author Biography Carl Osipov has been working in the information technology industry since 2001, with a focus on projects in big data analytics and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless cloud computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the worlds foremost experts in machine learning and helped manage the companys efforts to democratize artificial intelligence with Google Cloud and TensorFlow. Carl is an author of over 20 articles in professional, trade, and academic journals; an inventor with six patents at USPTO; and the holder of three corporate technology awards from IBM. Details ISBN1617297763 Author Carl Osipov Short Title Cloud Native Machine Learning Pages 250 Language English ISBN-10 1617297763 ISBN-13 9781617297762 Format Paperback Subtitle Deploying Pytorch Models on AWS Publisher Manning Publications Imprint Manning Publications Place of Publication New York Country of Publication United States Year 2022 AU Release Date 2022-03-16 NZ Release Date 2022-03-16 US Release Date 2022-03-16 Publication Date 2022-03-16 UK Release Date 2022-03-16 DEWEY 006.31 Audience General We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:135025486;
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Format: Paperback
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ISBN-13: 9781617297762
Author: Carl Osipov
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Book Title: MLOps Engineering at Scale
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