Implementing MLOps in the Enterprise a production-first approach
Description
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
- Build production applications with LLMs and Generative AI, while reducing risks, increasing the efficiency, and fine tuning models
- 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
More Details
Format
Language
English
ISBN
9781098136543, 1098136543, 9781098136550, 1098136551
Notes
General Note
Description based upon print version of record.
General Note
Using Feast Feature Store
Description
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.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Haviv, Y., & Gift, N. (2023). Implementing MLOps in the Enterprise: a production-first approach . O'Reilly Media, Inc..
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Haviv, Yaron and Noah, Gift. 2023. Implementing MLOps in the Enterprise: A Production-first Approach. Sebastopol, CA: O'Reilly Media, Inc.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Haviv, Yaron and Noah, Gift. Implementing MLOps in the Enterprise: A Production-first Approach Sebastopol, CA: O'Reilly Media, Inc, 2023.
Harvard Citation (style guide)Haviv, Y. and Gift, N. (2023). Implementing mlops in the enterprise: a production-first approach. Sebastopol, CA: O'Reilly Media, Inc.
MLA Citation, 9th Edition (style guide)Haviv, Yaron,, and Noah Gift. Implementing MLOps in the Enterprise: A Production-first Approach O'Reilly Media, Inc., 2023.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
Staff View
Grouped Work ID
a57987e1-f96c-38ab-e4ee-118b324c47b5-eng
Grouping Information
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| Full title | implementing mlops in the enterprise a production first approach |
| Author | haviv yaron |
| Grouping Category | book |
| Last Update | 2025-01-24 12:33:29PM |
| Last Indexed | 2025-05-22 03:31:41AM |
Book Cover Information
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| First Loaded | Dec 17, 2024 |
| Last Used | Jan 4, 2025 |
Marc Record
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| Last File Modification Time | Dec 17, 2024 08:26:52 AM |
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| 500 | |a Description based upon print version of record. | ||
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| 505 | 0 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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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