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Pragmatic AI

Author : Noah Gift

Publisher : Addison-Wesley Professional

Page : 720 pages

File Size : 49,56 MB

Release : 2018-07-12

Category : Computers

ISBN : 0134863917

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Book Description

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 demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.



Introducing MLOps

Author : Mark Treveil

Publisher : "O'Reilly Media, Inc."

Page : 171 pages

File Size : 32,87 MB

Release : 2020-11-30

Category : Computers

ISBN : 1098116429

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Book Description

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, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized



Engineering MLOps

Author : Emmanuel Raj

Publisher : Packt Publishing Ltd

Page : 370 pages

File Size : 44,3 MB

Release : 2021-04-19

Category : Computers

ISBN : 1800566328

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Book Description

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.



Machine Learning Engineering in Action

Author : Ben Wilson

Publisher : Simon and Schuster

Page : 879 pages

File Size : 43,34 MB

Release : 2022-05-17

Category : Computers

ISBN : 1638356580

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Book Description

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: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.



A Comprehensive Guide to Machine Learning Operations (MLOps)

Author : Rick Spair

Publisher : Rick Spair

Page : 98 pages

File Size : 17,42 MB

Release :

Category : Computers

ISBN :

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Book Description

Artificial Intelligence (AI) and Machine Learning (ML) are transforming industries, revolutionizing how businesses make decisions, automate processes, and provide innovative products and services. Yet, the successful implementation of AI and ML goes beyond developing sophisticated models. It requires the seamless integration of these models into operational workflows, ensuring their reliability, scalability, security, and ethical compliance. This integration is the heart of Machine Learning Operations or MLOps. This comprehensive guide is your passport to understanding the intricate world of MLOps. Whether you are an aspiring data scientist, a seasoned machine learning engineer, an operations professional, or a business leader, this guide is designed to equip you with the knowledge and insights needed to navigate the complexities of MLOps effectively.



Machine Learning Design Patterns

Author : Valliappa Lakshmanan

Publisher : O'Reilly Media

Page : 408 pages

File Size : 11,81 MB

Release : 2020-10-15

Category : Computers

ISBN : 1098115759

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Book Description

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 common problems throughout the ML process. These design patterns codify the experience of hundreds of experts into straightforward, approachable advice. In this book, you will find detailed explanations of 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the best technique for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure models are treating users fairly



Hybrid Cloud Infrastructure and Operations Explained

Author : Mansura Habiba

Publisher : Packt Publishing Ltd

Page : 344 pages

File Size : 26,13 MB

Release : 2022-08-29

Category : Computers

ISBN : 1803233672

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Book Description

Modernize and migrate smoothly to hybrid cloud infrastructure and successfully mitigate complexities relating to the infrastructure, platform, and production environment Key FeaturesPresents problems and solutions for application modernization based on real-life use casesHelps design and implement efficient, highly available, and scalable cloud-native applicationsTeaches you how to adopt a cloud-native culture for successful deployments on hybrid cloud platformsBook Description Most organizations are now either moving to the cloud through modernization or building their apps in the cloud. Hybrid cloud is one of the best approaches for cloud migration and the modernization journey for any enterprise. This is why, along with coding skills, developers need to know the big picture of cloud footprint and be aware of the integration models between apps in a hybrid and multi-cloud infrastructure. This book represents an overview of your end-to-end journey to the cloud. To be future agnostic, the journey starts with a hybrid cloud. You'll gain an overall understanding of how to approach migration to the cloud using hybrid cloud technologies from IBM and Red Hat. Next, you'll be able to explore the challenges, requirements (both functional and non-functional), and the process of app modernization for enterprises by analyzing various use cases. The book then provides you with insights into the different reference solutions for app modernization on the cloud, which will help you to learn how to design and implement patterns and best practices in your job. By the end of this book, you'll be able to successfully modernize applications and cloud infrastructure in hyperscaler public clouds such as IBM and hybrid clouds using Red Hat technologies as well as develop secure applications for cloud environments. What you will learnStrategize application modernization, from the planning to the implementation phaseApply cloud-native development concepts, methods, and best practicesSelect the right strategy for cloud adoption and modernizationExplore container platforms, storage, network, security, and operationsManage cloud operations using SREs, FinOps, and MLOps principlesDesign a modern data insight hub on the cloudWho this book is for This book is for cloud-native application developers involved in modernizing legacy applications by refactoring and rebuilding them. Cloud solution architects and technical leaders will also find this book useful. It will be helpful to have a basic understanding of cloud-native application development and cloud providers before getting started with this book.



MLOps Engineering at Scale

Author : Carl Osipov

Publisher : Simon and Schuster

Page : 497 pages

File Size : 34,6 MB

Release : 2022-03-22

Category : Computers

ISBN : 1638356505

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Book Description

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, transforming, and loading datasets Querying datasets with SQL Understanding automatic differentiation in PyTorch Deploying model training pipelines as a service endpoint Monitoring and managing your pipeline’s life cycle Measuring performance improvements MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities. About the technology A production-ready machine learning system includes efficient data pipelines, integrated monitoring, and means to scale up and down based on demand. Using cloud-based services to implement ML infrastructure reduces development time and lowers hosting costs. Serverless MLOps eliminates the need to build and maintain custom infrastructure, so you can concentrate on your data, models, and algorithms. About the book MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production. What's inside Reduce or eliminate ML infrastructure management Learn state-of-the-art MLOps tools like PyTorch Lightning and MLFlow Deploy training pipelines as a service endpoint Monitor and manage your pipeline’s life cycle Measure performance improvements About the reader Readers need to know Python, SQL, and the basics of machine learning. No cloud experience required. About the author Carl Osipov implemented his first neural net in 2000 and has worked on deep learning and machine learning at Google and IBM. Table of Contents PART 1 - MASTERING THE DATA SET 1 Introduction to serverless machine learning 2 Getting started with the data set 3 Exploring and preparing the data set 4 More exploratory data analysis and data preparation PART 2 - PYTORCH FOR SERVERLESS MACHINE LEARNING 5 Introducing PyTorch: Tensor basics 6 Core PyTorch: Autograd, optimizers, and utilities 7 Serverless machine learning at scale 8 Scaling out with distributed training PART 3 - SERVERLESS MACHINE LEARNING PIPELINE 9 Feature selection 10 Adopting PyTorch Lightning 11 Hyperparameter optimization 12 Machine learning pipeline



Mathematics for Machine Learning

Author : Marc Peter Deisenroth

Publisher : Cambridge University Press

Page : 392 pages

File Size : 38,46 MB

Release : 2020-04-23

Category : Computers

ISBN : 1108569323

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Book Description

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.



Practical MLOps

Author : Noah Gift

Publisher : "O'Reilly Media, Inc."

Page : 461 pages

File Size : 16,34 MB

Release : 2021-09-14

Category : Computers

ISBN : 1098102983

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Book Description

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. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start. You'll discover how to: Apply DevOps best practices to machine learning Build production machine learning systems and maintain them Monitor, instrument, load-test, and operationalize machine learning systems Choose the correct MLOps tools for a given machine learning task Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware