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Practical MLOps
ویرایش: نویسندگان: Noah Gift, Alfredo Deza سری: ISBN (شابک) : 9781098103019, 9781098102944 ناشر: O'Reilly Media, Inc. سال نشر: 2021 تعداد صفحات: زبان: English فرمت فایل : EPUB (درصورت درخواست کاربر به PDF، EPUB یا AZW3 تبدیل می شود) حجم فایل: 83 Mb
قیمت کتاب (تومان) : 45,000
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توجه داشته باشید کتاب MLO های عملی نسخه زبان اصلی می باشد و کتاب ترجمه شده به فارسی نمی باشد. وبسایت اینترنشنال لایبرری ارائه دهنده کتاب های زبان اصلی می باشد و هیچ گونه کتاب ترجمه شده یا نوشته شده به فارسی را ارائه نمی دهد.
توضیحاتی در مورد کتاب MLO های عملی
تولید مدل های شما چالش اساسی یادگیری ماشین است. MLOps مجموعه ای از اصول اثبات شده را با هدف حل این مشکل به روشی قابل اعتماد و خودکار ارائه می دهد. این راهنمای روشنگر شما را از چیستی MLOps (و تفاوت آن با DevOps) می‌آموزد و به شما نشان می‌دهد که چگونه آن را برای عملیاتی کردن مدل‌های یادگیری ماشین خود به‌کار بگیرید. مهندسان فعلی و مشتاق یادگیری ماشین - یا هر کسی که با علم داده و پایتون آشنا باشد - پایه‌ای در ابزارها و روش‌های MLOps (همراه با AutoML و نظارت و ثبت‌نام) ایجاد خواهند کرد، سپس نحوه پیاده‌سازی آنها را در AWS، Microsoft Azure و Google Cloud. هرچه سریع‌تر سیستم یادگیری ماشینی را ارائه دهید که کار می‌کند، سریع‌تر می‌توانید روی مشکلات تجاری که می‌خواهید از بین ببرید تمرکز کنید. این کتاب به شما یک شروع می دهد. شما خواهید فهمید که چگونه می توانید: بهترین شیوه های DevOps را در یادگیری ماشین بکار ببرید سیستم های یادگیری ماشینی تولید بسازید و آنها را حفظ کنید نظارت، ابزار، تست بارگذاری، و عملیاتی کردن سیستم های یادگیری ماشین انتخاب ابزار MLOps صحیح برای یک کار خاص یادگیری ماشینی، اجرای یادگیری ماشینی مدل ها بر روی پلتفرم ها و دستگاه های مختلف، از جمله تلفن های همراه و سخت افزارهای تخصصی
توضیحاتی درمورد کتاب به خارجی
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
فهرست مطالب
Cover Copyright Table of Contents Preface Why We Wrote This Book How This Book Is Organized Chapters Appendixes Exercise Questions Discussion Questions Origin of Chapter Quotes Conventions Used in This Book Using Code Examples O’Reilly Online Learning How to Contact Us Acknowledgments From Noah From Alfredo Chapter 1. Introduction to MLOps Rise of the Machine Learning Engineer and MLOps What Is MLOps? DevOps and MLOps An MLOps Hierarchy of Needs Implementing DevOps Configuring Continuous Integration with GitHub Actions DataOps and Data Engineering Platform Automation MLOps Conclusion Exercises Critical Thinking Discussion Questions Chapter 2. MLOps Foundations Bash and the Linux Command Line Cloud Shell Development Environments Bash Shell and Commands List Files Run Commands Files and Navigation Input/Output Configuration Writing a Script Cloud Computing Foundations and Building Blocks Getting Started with Cloud Computing Python Crash Course Minimalistic Python Tutorial Math for Programmers Crash Course Descriptive Statistics and Normal Distributions Optimization Machine Learning Key Concepts Doing Data Science Build an MLOps Pipeline from Zero Conclusion Exercises Critical Thinking Discussion Questions Chapter 3. MLOps for Containers and Edge Devices Containers Container Runtime Creating a Container Running a Container Best Practices Serving a Trained Model Over HTTP Edge Devices Coral Azure Percept TFHub Porting Over Non-TPU Models Containers for Managed ML Systems Containers in Monetizing MLOps Build Once, Run Many MLOps Workflow Conclusion Exercises Critical Thinking Discussion Questions Chapter 4. Continuous Delivery for Machine Learning Models Packaging for ML Models Infrastructure as Code for Continuous Delivery of ML Models Using Cloud Pipelines Controlled Rollout of Models Testing Techniques for Model Deployment Conclusion Exercises Critical Thinking Discussion Questions Chapter 5. AutoML and KaizenML AutoML MLOps Industrial Revolution Kaizen Versus KaizenML Feature Stores Apple’s Ecosystem Apple’s AutoML: Create ML Apple’s Core ML Tools Google’s AutoML and Edge Computer Vision Azure’s AutoML AWS AutoML Open Source AutoML Solutions Ludwig FLAML Model Explainability Conclusion Exercises Critical Thinking Discussion Questions Chapter 6. Monitoring and Logging Observability for Cloud MLOps Introduction to Logging Logging in Python Modifying Log Levels Logging Different Applications Monitoring and Observability Basics of Model Monitoring Monitoring Drift with AWS SageMaker Monitoring Drift with Azure ML Conclusion Exercises Critical Thinking Discussion Questions Chapter 7. MLOps for AWS Introduction to AWS Getting Started with AWS Services MLOps on AWS MLOps Cookbook on AWS CLI Tools Flask Microservice AWS Lambda Recipes AWS Lambda-SAM Local AWS Lambda-SAM Containerized Deploy Applying AWS Machine Learning to the Real World Conclusion Exercises Critical Thinking Discussion Questions Chapter 8. MLOps for Azure Azure CLI and Python SDK Authentication Service Principal Authenticating API Services Compute Instances Deploying Registering Models Versioning Datasets Deploying Models to a Compute Cluster Configuring a Cluster Deploying a Model Troubleshooting Deployment Issues Retrieving Logs Application Insights Debugging Locally Azure ML Pipelines Publishing Pipelines Azure Machine Learning Designer ML Lifecycle Conclusion Exercises Critical Thinking Discussion Questions Chapter 9. MLOps for GCP Google Cloud Platform Overview Continuous Integration and Continuous Delivery Kubernetes Hello World Cloud Native Database Choice and Design DataOps on GCP: Applied Data Engineering Operationalizing ML Models Conclusion Exercises Critical Thinking Discussion Questions Chapter 10. Machine Learning Interoperability Why Interoperability Is Critical ONNX: Open Neural Network Exchange ONNX Model Zoo Convert PyTorch into ONNX Create a Generic ONNX Checker Convert TensorFlow into ONNX Deploy ONNX to Azure Apple Core ML Edge Integration Conclusion Exercises Critical Thinking Discussion Questions Chapter 11. Building MLOps Command Line Tools and Microservices Python Packaging The Requirements File Command Line Tools Creating a Dataset Linter Modularizing a Command Line Tool Microservices Creating a Serverless Function Authenticating to Cloud Functions Building a Cloud-Based CLI Machine Learning CLI Workflows Conclusion Exercises Critical Thinking Discussion Questions Chapter 12. Machine Learning Engineering and MLOps Case Studies Unlikely Benefits of Ignorance in Building Machine Learning Models MLOps Projects at Sqor Sports Social Network Mechanical Turk Data Labeling Influencer Rank Athlete Intelligence (AI Product) The Perfect Technique Versus the Real World Critical Challenges in MLOps Ethical and Unintended Consequences Lack of Operational Excellence Focus on Prediction Accuracy Versus the Big Picture Final Recommendations to Implement MLOps Data Governance and Cybersecurity MLOps Design Patterns Conclusion Exercises Critical Thinking Discussion Questions Appendix A. Key Terms Appendix B. Technology Certifications AWS Certifications AWS Cloud Practitioner and AWS Solutions Architect AWS Certified Machine Learning - Specialty Other Cloud Certifications Azure Data Scientist and AI Engineer GCP SQL-Related Certifications Appendix C. Remote Work Equipment for Working Remotely Network Home Work Area Location, Location, Location Appendix D. Think Like a VC for Your Career Pear Revenue Strategy Passive Positive Exponential Autonomy Rule of 25% Notes Appendix E. Building a Technical Portfolio for MLOps Project: Continuous Delivery of Flask/FastAPI Data Engineering API on a PaaS Platform Project: Docker and Kubernetes Container Project Project: Serverless AI Data Engineering Pipeline Project: Build Edge ML Solution Project: Build Cloud Native ML Application or API Getting a Job: Don’t Storm the Castle, Walk in the Backdoor Appendix F. Data Science Case Study: Intermittent Fasting Notes on Intermittent Fasting, Blood Glucose, and Food Appendix G. Additional Educational Resources Additional MLOps Critical Thinking Questions Additional MLOps Educational Materials Education Disruption Current State of Higher Education That Will Be Disrupted 10X Better Education Conclusion Appendix H. Technical Project Management Project Plan Weekly Demo Task Tracking Index About the Authors Colophon
