Engineering MLOps Book [PDF] Download

Download the fantastic book titled Engineering MLOps written by Emmanuel Raj, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Engineering MLOps", which was released on 19 April 2021. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.

Summary of Engineering MLOps by Emmanuel Raj PDF

Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook Description Engineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll learn how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitor pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is for This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book.


Detail About Engineering MLOps PDF

  • Author : Emmanuel Raj
  • Publisher : Packt Publishing Ltd
  • Genre : Computers
  • Total Pages : 370 pages
  • ISBN : 1800566328
  • PDF File Size : 26,7 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Engineering MLOps

Engineering MLOps
  • Publisher : Packt Publishing Ltd
  • File Size : 38,9 Mb
  • Release Date : 19 April 2021
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Get up and running with machine learning life cycle management and implement MLOps in your organization Key FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models

MLOps Engineering at Scale

MLOps Engineering at Scale
  • Publisher : Simon and Schuster
  • File Size : 52,9 Mb
  • Release Date : 22 March 2022
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Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools! In MLOps Engineering at Scale you will learn: Extracting,

Machine Learning Engineering in Action

Machine Learning Engineering in Action
  • Publisher : Simon and Schuster
  • File Size : 39,7 Mb
  • Release Date : 26 April 2022
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Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn:

Introducing MLOps

Introducing MLOps
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 49,5 Mb
  • Release Date : 30 November 2020
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More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical,

Practical MLOps

Practical MLOps
  • Publisher : Unknown Publisher
  • File Size : 51,7 Mb
  • Release Date : 21 December 2021
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Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way.

Machine Learning Engineering with Python

Machine Learning Engineering with Python
  • Publisher : Packt Publishing Ltd
  • File Size : 36,5 Mb
  • Release Date : 05 November 2021
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Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments Key Features Explore hyperparameter optimization and model management tools

Machine Learning Design Patterns

Machine Learning Design Patterns
  • Publisher : O'Reilly Media
  • File Size : 23,9 Mb
  • Release Date : 15 October 2020
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The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle

Machine Learning Engineering with MLflow

Machine Learning Engineering with MLflow
  • Publisher : Packt Publishing Ltd
  • File Size : 53,5 Mb
  • Release Date : 27 August 2021
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Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key FeaturesExplore machine learning workflows for stating ML problems in a

Pragmatic AI

Pragmatic AI
  • Publisher : Addison-Wesley Professional
  • File Size : 20,5 Mb
  • Release Date : 12 July 2018
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Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift

TinyML

TinyML
  • Publisher : O'Reilly Media
  • File Size : 20,8 Mb
  • Release Date : 16 December 2019
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Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With