Training Data for Machine Learning

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• Preface
  • Who Should Read This Book?
    • For the Technical Professional and Engineer
    • For the Manager and Director
    • For the Subject Matter Expert and Data Annotation Specialist
    • For the Data Scientist
  • Why I Wrote This Book
  • How This Book Is Organized
  • Themes
    • The Basics and Getting Started
    • Concepts and Theories
    • Putting It All Together
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• 1. Training Data Introduction
  • Training Data Intents
    • What Can You Do With Training Data?
    • What Is Training Data Most Concerned With?
      • Schema
      • Raw data
      • Annotations
      • Quality
      • Integrations
      • The human role
  • Training Data Opportunities
    • Business Transformation
    • Training Data Efficiency
    • Tooling Proficiency
    • Process Improvement Opportunities
  • Why Training Data Matters
    • ML Applications Are Becoming Mainstream
    • The Foundation of Successful AI
    • Training Data Is Here to Stay
    • Training Data Controls the ML Program
    • New Types of Users
  • Training Data in the Wild
    • What Makes Training Data Difficult?
    • The Art of Supervising Machines
    • A New Thing for Data Science
    • ML Program Ecosystem
      • Raw data media types
    • Data-Centric Machine Learning
    • Failures
    • History of Development Affects Training Data Too
    • What Training Data Is Not
  • Generative AI
    • Human Alignment Is Human Supervision
  • Summary
• 2. Getting Up and Running
  • Introduction
  • Getting Up and Running
    • Installation
    • Tasks Setup
    • Annotator Setup
      • Portal (default)
      • Embedded
    • Data Setup
    • Workflow Setup
    • Data Catalog Setup
    • Initial Usage
    • Optimization
  • Tools Overview
    • Training Data for Machine Learning
    • Growing Selection of Tools
    • People, Process, and Data
    • Embedded Supervision
    • Human Computer Supervision
    • Separation of End Concerns
    • Standards
    • Many Personas
    • A Paradigm to Deliver Machine Learning Software
  • Trade-Offs
    • Costs
    • Installed Versus Software as a Service
    • Development System
      • Sequentially dependent discoveries
    • Scale
      • Why is it useful to define scale?
      • Transitioning from small to medium scale
      • Large-scale thoughts
    • Installation Options
      • Packaging
      • Storage
      • Database
      • Data configuration
    • Annotation Interfaces
    • Modeling Integration
    • Multi-User versus Single-User Systems
    • Integrations
    • Scope
      • Platform and suite solutions
      • Decision-making process
      • Cautions
      • Point solutions
      • Tools in between
    • Hidden Assumptions
    • Security
      • Security architecture
      • Attack surface
      • Security configuration
      • Security benefits
      • User access
      • Data science access
      • Root-level access
    • Open Source and Closed Source
      • Choose an open source tool to get up and running quickly
      • See the forest from the trees
      • Capability over optimizations
      • Ease of use in different flows
      • Vastly different assumptions
      • Look at settings, not first impressions
      • Is it easy to use, or just lacking features?
      • Customization is the name of the game
  • History
    • Open Source Standards
    • Realizing the Need for Dedicated Tooling
      • More usage, more demands
      • Advent of new standards
  • Summary
• 3. Schema
  • Schema Deep Dive Introduction
  • Labels and AttributesWhat Is It?
    • What Do We Care About?
    • Introduction to Labels
    • Attributes Introduction
      • Attribute concepts
      • Schema complexity trade-off
      • Attribute depth
    • Attribute Complexity Exceeds Spatial Complexity
      • The hidden background case
      • Example of sharing attributes between labels
    • Technical Overview
      • Example of an attribute in relation to an instance
      • Data representations for engineering
      • Examples of attributes
      • Technical example of an attribute
  • Spatial RepresentationWhere Is It?
    • Using Spatial Types to Prevent Social Bias
      • One way to avoid spatial bias
      • Joint responsibility
    • Trade-Offs with Types
    • Computer Vision Spatial Type Examples
      • Full image tag
      • Box (2D)
      • Polygon
      • Ellipse and circle
      • Cuboid
      • Types with multiple uses
      • Other types
      • Raster mask
      • Polygons and raster masks
      • Keypoint geometry
      • Custom spatial templates
      • Complex spatial types
  • Relationships, Sequences, Time Series: When Is It?
    • Sequences and Relationships
    • When
  • Guides and Instructions
    • Judgment Calls
  • Relation of Machine Learning Tasks to Training Data
    • Semantic Segmentation
    • Image Classification (Tags)
    • Object Detection
    • Pose Estimation
    • Relationship of Tasks to Training Data Types
  • General Concepts
    • Instance Concept Refresher
    • Upgrading Data Over Time
    • The Boundary Between Modeling and Training Data
    • Raw Data Concepts
  • Summary
• 4. Data Engineering
  • Introduction
    • Who Wants the Data?
      • Annotators
      • Data scientists
      • ML programs
      • Application engineers
      • Other stakeholders
    • A Game of Telephone
      • When a system of record is needed
    • Planning a Great System
    • Naive and Training DataCentric Approaches
      • Naive approaches
      • Training datacentric (system of record)
      • The first steps
  • Raw Data Storage
    • By Reference or by Value
    • Off-the-Shelf Dedicated Training Data Tooling on Your Own Hardware
    • Data Storage: Where Does the Data Rest?
    • External Reference Connection
    • Raw Media (BLOB)Type Specific
      • Images
      • Video
      • 3D
      • Text
      • Medical
      • Geospatial
  • Formatting and Mapping
    • User-Defined Types (Compound Files)
    • Defining DataMaps
    • Ingest Wizards
    • Organizing Data and Useful Storage
    • Remote Storage
    • Versioning
      • Per-instance history
      • Per file and per set
      • Per-export snapshots
  • Data Access
    • Disambiguating Storage, Ingestion, Export, and Access
    • File-Based Exports
    • Streaming Data
      • Streaming benefits
      • Streaming drawbacks
      • Example: Fetch and stream
    • Queries Introduction
    • Integrations with the Ecosystem
  • Security
    • Access Control
    • Identity and Authorization
    • Example of Setting Permissions
    • Signed URLs
      • Cloud connections and signed URLs
    • Personally Identifiable Information
      • PII-compliant data chain
        • PII avoidance
        • PII removal
  • Pre-Labeling
    • Updating Data
      • Pre-labeling gotchas
      • Pre-labeling data prep process
  • Summary
• 5. Workflow
  • Introduction
  • Glue Between Tech and People
    • Why Are Human Tasks Needed?
    • Partnering with Non-Software Users in New Ways
  • Getting Started with Human Tasks
    • Basics
    • Schemas Staying Power
    • User Roles
    • Training
    • Gold Standard Training
    • Task Assignment Concepts
    • Do You Need to Customize the Interface?
    • How Long Will the Average Annotator Be Using It?
    • Tasks and Project Structure
  • Quality Assurance
    • Annotator Trust
    • Annotators Are Partners
      • Who supervises the data
      • All training data has errors
      • Annotator needs
    • Common Causes of Training Data Errors
    • Task Review Loops
      • Standard review loop
      • Consensus
  • Analytics
    • Annotation Metrics Examples
    • Data Exploration
      • Data exploration tool example
      • Explore processes
      • Explore examples
      • Similar image reduction
  • Models
    • Using the Model to Debug the Humans
    • Distinctions Between a Dataset, Model, and Model Run
    • Getting Data to Models
  • Dataflow
    • Overview of Streaming
    • Data Organization
      • Folders and static organization
      • Filters and dynamic organization
    • Pipelines and Processes
      • The dataset connection
      • Sending a single file to that set
      • Relating a dataset to a template
      • Putting the whole example together
      • Expanding the example
      • Non-linear example
      • Hooks
  • Direct Annotation
    • Business Process Integration
    • Attributes
    • Depth of Labeling
    • Supervising Existing Data
    • Interactive Automations
    • Example: Semantic Segmentation Auto Bordering
    • Video
      • Motion
      • Examples of tracking objects through time (time series)
      • Static objects
      • Persistent objects: football example
      • Series example
      • Video events
      • Detecting sequence errors
      • Common issues in video annotation
  • Summary
• 6. Theories, Concepts, and Maintenance
  • Introduction
  • Theories
    • A System Is Only as Useful as Its Schema
    • Who Supervises the Data Matters
    • Intentionally Chosen Data Is Best
    • Working with Historical Data
    • Training Data Is Like Code
    • Surface Assumptions Around Usage of Your Training Data
      • Use definitions and processes to protect against assumptions
    • Human Supervision Is Different from Classic Datasets
      • Discovery versus automation
      • Discovery
  • General Concepts
    • Data Relevancy
      • Overall system design
      • Raw data collection
    • Need for Both Qualitative and Quantitative Evaluations
    • Iterations
    • Prioritization: What to Label
    • Transfer Learnings 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
    • Simple Schema for a Strawberry Picking System
    • Geometric Representations
    • Binary Classification
    • Lets Manually Create Our First Set
    • Upgraded Classification
    • Where Is the Traffic Light?
  • Maintenance
    • Actions
      • Increase schema depth to improve performance
      • Better align the spatial type to the raw data
      • Create more tasks
      • Change the raw data
    • Net Lift
    • Levels of System Maturity of Training Data Operations
    • Applied Versus Research Sets
  • Training Data Management
    • Quality
    • Completed Tasks
    • Freshness
    • Maintaining Set Metadata
    • Task 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
    • New Expectations People Have for the Future of AI
    • Sometimes Proposals and Corrections, Sometimes Replacement
    • Upstream Producers and Downstream Consumers
      • Producer and consumer comparison
      • Producer and consumer mindset
      • Why is new structure needed?
      • The budget
      • The AI Directors background
      • Director of Training Data role
      • AI-focused company modifications
      • Classic company modification
    • Spectrum of Training Data Team Engagement
    • Dedicated Producers and Other Teams
    • Organizing Producers from Other Teams
      • Director of AI data responsibilities
      • Training Data Evangelist
      • Training Data Production Manager(s)
      • Annotation Producer
      • Data Engineer
  • Use Case Discovery
    • Rubric for Good Use Cases
      • Detailed rubric
      • Adds a new capability use case
      • Repeating use cases
      • Specialists and experts
    • Evaluating a Use Case Against the Rubric
      • Automatic background removal
      • Evaluation example
    • Conceptual Effects of Use Cases
      • Ongoing impact of use cases
  • The New Crowd Sourcing: Your Own Experts
    • Key Levers on Training Data ROI
    • What the Annotated Data Represents
    • Trade-Offs of Controlling Your Own Training Data
    • The Need for Hardware
    • Common Project Mistakes
  • Modern Training Data Tools
    • Think Learning Curve, Not Perfection
    • New Training and Knowledge Are Required
      • Everyone
      • Annotators
      • Managers
      • Executives
    • How Companies Produce and Consume Data
    • Trap to Avoid: Premature Optimization in Training Data
    • No Silver Bullets
    • Culture of Training Data
    • New Engineering Principles
  • Summary
• 8. Automation
  • Introduction
  • Getting Started
    • Motivation: When to Use These Methods?
    • Check What Part of the Schema a Method Is Designed to Work On
    • What Do People Actually Use?
      • Commonly used techniques
      • Domain-specific
      • A note on ordering
    • What Kind of Results Can I Expect?
    • Common Confusions
      • Fully automatic labeling for novel model creation
      • Proprietary automatic methods
    • User Interface Optimizations
    • Risks
  • Trade-Offs
    • Nature of Automations
    • Setup Costs
    • How to Benchmark Well
    • How to Scope the Automation Relative to the Problem
    • Correction Time
    • Subject Matter Experts
    • Consider How the Automations Stack
  • Pre-Labeling
    • Standard Pre-Labeling
      • Benefits
      • Caveats
    • Pre-Labeling a Portion of the Data Only
      • Use off-the-shelf models
      • Clear separation of concerns
      • The one step early trick
      • How to get started pre-labeling
  • Interactive Annotation Automation
    • Creating Your Own
    • Technical Setup Notes
    • What Is a Watcher? (Observer Pattern)
    • How to Use a Watcher
    • Interactive Capturing of a Region of Interest
    • Interactive Drawing Box to Polygon Using GrabCut
    • Full Image Model Prediction Example
    • Example: Person Detection for Different Attribute
  • Quality Assurance Automation
    • Using the Model to Debug the Humans
    • Automated Checklist Example
    • Domain-Specific Reasonableness Checks
  • Data Discovery: What to Label
    • Human Exploration
    • Raw Data Exploration
    • Metadata Exploration
    • Adding Pre-Labeling-Based Metadata
  • Augmentation
    • Better Models Are Better than Better Augmentation
    • To Augment or Not to Augment
      • Training/runtime augmentation
      • Patch and inject method (crop and inject)
  • Simulation and Synthetic Data
    • Simulations Still Need Human Review
  • Media Specific
    • What Methods Work with Which Media?
    • Considerations
    • Media-Specific Research
  • Domain Specific
    • Geometry-Based Labeling
      • Multi-sensor labeling automationspatial
      • Spatial labeling
    • Heuristics-Based Labeling
  • Summary
• 9. Case Studies and Stories
  • Introduction
  • Industry
    • A Security Startup Adopts Training Data Tools
    • Quality Assurance at a Large-Scale Self-Driving Project
      • Tricky schemas should be expanded, not shrunk
      • Dont justify a clearly bad schema with domain-specific assumptions
      • Tracking spatial quality and errors per image
      • Regression and focused effort do not always solve specific problems
      • Overfocus on complex instructions instead of fixing the schema
      • Trade-offs of attempting to achieve perfection in nuanced domain-specific cases
      • Understanding nuanced cases
      • Learning from mistakes
        • Define occlusion well
        • Expand schemas
        • Remember the null case
        • Missing assumptions for language barriers
        • Dont overfocus on spatial information
    • Big-Tech Challenges
      • Two annotation software teams
      • Confusing the media types
      • Non-queryable
      • Different teams for annotations and raw media
      • Moving toward a system of record
      • Missing the big picture
      • Solution
      • Lets address loops
      • Human in the loop
      • The case for aligning teams around training data
    • Insurance Tech Startup Lessons
      • Will the production data match the training data?
      • Too late to bring in training data software
    • Stories
      • Static Schema Prevented Innovation at Self Driving Firm
      • Startup Didnt Change Schema and Wasted Effort
      • Accident Prevention Startup Missed Data-Centric Approach
      • Sports Startup Successfully Used Pre-Labeling
  • An Academic Approach to Training Data
    • Kaggle TSA Competition
      • Keying in on training data
      • How focusing on training data reveals commercial efficiencies
      • Learning lessons and mistakes
  • Summary
• Index

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