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|a 9781098136550
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|a Haviv, Yaron
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| 245 |
0 |
0 |
|a Implementing MLOps in the Enterprise
|h [electronic resource]
|b a production-first approach
|c Yaron Haviv and Noah Gift
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| 260 |
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|a Sebastopol, CA
|b O'Reilly Media, Inc.
|c 2023
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| 300 |
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|a 380 p.
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| 505 |
0 |
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|a MLOps in the Cloud -- Key Cloud Development Environments -- The Key Players in Cloud Computing -- MLOps On-Premises -- MLOps in Hybrid Environments -- Enterprise MLOps Strategy -- Conclusion -- Critical Thinking Discussion Questions -- Exercises -- Chapter 2. The Stages of MLOps -- Getting Started -- Choose Your Algorithm -- Design Your Pipelines -- Data Collection and Preparation -- Data Storage and Ingestion -- Data Exploration and Preparation -- Data Labeling -- Feature Stores -- Model Development and Training -- Writing and Maintaining Production ML Code
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| 505 |
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|a Cover -- Copyright -- Table of Contents -- Preface -- Who This Book Is For -- Navigating This Book -- Conventions Used in This Book -- Using Code Examples -- O'Reilly Online Learning -- How to Contact Us -- Acknowledgments -- Yaron -- Noah -- Chapter 1. MLOps: What Is It and Why Do We Need It? -- What Is MLOps? -- MLOps in the Enterprise -- Understanding ROI in Enterprise Solutions -- Understanding Risk and Uncertainty in the Enterprise -- MLOps Versus DevOps -- What Isn't MLOps? -- Mainstream Definitions of MLOps -- What Is ML Engineering? -- MLOps and Business Incentives
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| 505 |
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|a Chapter 3. Getting Started with Your First MLOps Project -- Identifying the Business Use Case and Goals -- Finding the AI Use Case -- Defining Goals and Evaluating the ROI -- How to Build a Successful ML Project -- Approving and Prototyping the Project -- Scaling and Productizing Projects -- Project Structure and Lifecycle -- ML Project Example from A to Z -- Exploratory Data Analysis -- Data and Model Pipeline Development -- Application Pipeline Development -- Scaling and Productizing the Project -- CI/CD and Continuous Operations -- Conclusion -- Critical Thinking Discussion Questions
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| 505 |
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|a Tracking and Comparing Experiment Results -- Distributed Training and Hyperparameter Optimization -- Building and Testing Models for Production -- Deployment (and Online ML Services) -- From Model Endpoints to Application Pipelines -- Online Data Preparation -- Continuous Model and Data Monitoring -- Monitoring Data and Concept Drift -- Monitoring Model Performance and Accuracy -- The Strategy of Pretrained Models -- Building an End-to-End Hugging Face Application -- Flow Automation (CI/CD for ML) -- Conclusion -- Critical Thinking Discussion Questions -- Exercises
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| 505 |
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|a Exercises -- Chapter 4. Working with Data and Feature Stores -- Data Versioning and Lineage -- How It Works -- Common ML Data Versioning Tools -- Data Preparation and Analysis at Scale -- Structured and Unstructured Data Transformations -- Distributed Data Processing Architectures -- Interactive Data Processing -- Batch Data Processing -- Stream Processing -- Stream Processing Frameworks -- Feature Stores -- Feature Store Architecture and Usage -- Ingestion and Transformation Service -- Feature Storage -- Feature Retrieval (for Training and Serving) -- Feature Stores Solutions and Usage Example
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|a Information technology / Management / fast / (OCoLC)fst00973112
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|a Apprentissage automatique
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|a Information technology / Management / http://id.loc.gov/authorities/subjects/sh2008006980
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|a Machine learning / fast / (OCoLC)fst01004795
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|a Gift, Noah
|e author
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|a O'Reilly
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|a Description based upon print version of record. - Using Feast Feature Store
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|u https://learning.oreilly.com/library/view/~/9781098136574/?ar
|x Verlag
|3 Volltext
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|a With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: Learn the MLOps process, including its technological and business value Build and structure effective MLOps pipelines Efficiently scale MLOps across your organization Explore common MLOps use cases Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI Learn how to prepare for and adapt to the future of MLOps Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy
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