Book description
Your training data has as much to do with the success of your data project as the algorithms themselves because most failures in AI systems relate to training data. But while training data is the foundation for successful AI and machine learning, there are few comprehensive resources to help you ace the process.
In this hands-on guide, author Anthony Sarkis--lead engineer for the Diffgram AI training data software--shows technical professionals, managers, and subject matter experts how to work with and scale training data, while illuminating the human side of supervising machines. Engineering leaders, data engineers, and data science professionals alike will gain a solid understanding of the concepts, tools, and processes they need to succeed with training data.
With this book, you'll learn how to:
- Work effectively with training data including schemas, raw data, and annotations
- Transform your work, team, or organization to be more AI/ML data-centric
- Clearly explain training data concepts to other staff, team members, and stakeholders
- Design, deploy, and ship training data for production-grade AI applications
- Recognize and correct new training-data-based failure modes such as data bias
- Confidently use automation to more effectively create training data
- Successfully maintain, operate, and improve training data systems of record
Publisher resources
Table of contents
- Preface
- 1. Training Data Introduction
- 2. Getting Up and Running
- 3. Schema
- 4. Data Engineering
- 5. Workflow
-
6. Theories, Concepts, and Maintenance
- Introduction
- Theories
-
General Concepts
- Data Relevancy
- Need for Both Qualitative and Quantitative Evaluations
- Iterations
- Prioritization: What to Label
- Transfer Learning’s Relation to Datasets (Fine-Tuning)
- Per-Sample Judgment Calls
- Ethical and Privacy Considerations
- Bias
- Bias Is Hard to Escape
- Metadata
- Preventing Lost Metadata
- Train/Val/Test Is the Cherry on Top
- Sample Creation
- Maintenance
- Training Data Management
- Summary
-
7. AI Transformation and Use Cases
- Introduction
-
AI Transformation
- Seeing Your Day-to-Day Work as Annotation
- The Creative Revolution of Data-centric AI
- You Can Create New Data
- You Can Change What Data You Collect
- You Can Change the Meaning of the Data
- You Can Create!
- Think Step Function Improvement for Major Projects
- Build Your AI Data to Secure Your AI Present and Future
- Appoint a Leader: The Director of AI Data
- Use Case Discovery
- The New “Crowd Sourcing”: Your Own Experts
- Modern Training Data Tools
- Summary
- 8. Automation
- 9. Case Studies and Stories
- Index
- About the Author
Product information
- Title: Training Data for Machine Learning
- Author(s):
- Release date: November 2023
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492094524
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