Kubeflow for Machine Learning

Spis treści książki

• Foreword
• Preface
  • Our Assumption About You
  • Your Responsibility as a Practitioner
  • Conventions Used in This Book
  • Code Examples
    • Using Code Examples
  • OReilly Online Learning
  • How to Contact the Authors
  • How to Contact Us
  • Acknowledgments
  • Grievances
• 1. Kubeflow: What It Is and Who It Is For
  • Model Development Life Cycle
  • Where Does Kubeflow Fit In?
  • Why Containerize?
  • Why Kubernetes?
  • Kubeflows Design and Core Components
    • Data Exploration with Notebooks
    • Data/Feature Preparation
    • Training
    • Hyperparameter Tuning
    • Model Validation
    • Inference/Prediction
    • Pipelines
    • Component Overview
  • Alternatives to Kubeflow
    • Clipper (RiseLabs)
    • MLflow (Databricks)
    • Others
  • Introducing Our Case Studies
    • Modified National Institute of Standards and Technology
    • Mailing List Data
    • Product Recommender
    • CT Scans
  • Conclusion
• 2. Hello Kubeflow
  • Getting Set Up with Kubeflow
    • Installing Kubeflow and Its Dependencies
    • Setting Up Local Kubernetes
      • Minikube
    • Setting Up Your Kubeflow Development Environment
      • Setting up the Pipeline SDK
      • Setting up Docker
      • Editing YAML
    • Creating Our First Kubeflow Project
  • Training and Deploying a Model
    • Training and Monitoring Progress
    • Test Query
  • Going Beyond a Local Deployment
  • Conclusion
• 3. Kubeflow Design: Beyond the Basics
  • Getting Around the Central Dashboard
    • Notebooks (JupyterHub)
    • Training Operators
    • Kubeflow Pipelines
    • Hyperparameter Tuning
    • Model Inference
    • Metadata
    • Component Summary
  • Support Components
    • MinIO
    • Istio
    • Knative
    • Apache Spark
    • Kubeflow Multiuser Isolation
  • Conclusion
• 4. Kubeflow Pipelines
  • Getting Started with Pipelines
    • Exploring the Prepackaged Sample Pipelines
    • Building a Simple Pipeline in Python
    • Storing Data Between Steps
  • Introduction to Kubeflow Pipelines Components
    • Argo: the Foundation of Pipelines
    • What Kubeflow Pipelines Adds to Argo Workflow
    • Building a Pipeline Using Existing Images
    • Kubeflow Pipeline Components
  • Advanced Topics in Pipelines
    • Conditional Execution of Pipeline Stages
    • Running Pipelines on Schedule
  • Conclusion
• 5. Data and Feature Preparation
  • Deciding on the Correct Tooling
  • Local Data and Feature Preparation
    • Fetching the Data
    • Data Cleaning: Filtering Out the Junk
    • Formatting the Data
    • Feature Preparation
    • Custom Containers
  • Distributed Tooling
    • TensorFlow Extended
      • Keeping your data quality: TensorFlow data validation
      • TensorFlow Transform, with TensorFlow Extended on Beam
    • Distributed Data Using Apache Spark
      • Spark operators in Kubeflow
      • Reading the input data
      • Validating the schema
      • Handling missing fields
      • Filtering out bad data
      • Saving the output
    • Distributed Feature Preparation Using Apache Spark
  • Putting It Together in a Pipeline
  • Using an Entire Notebook as a Data Preparation Pipeline Stage
  • Conclusion
• 6. Artifact and Metadata Store
  • Kubeflow ML Metadata
    • Programmatic Query
    • Kubeflow Metadata UI
  • Using MLflows Metadata Tools with Kubeflow
    • Creating and Deploying an MLflow Tracking Server
    • Logging Data on Runs
    • Using the MLflow UI
  • Conclusion
• 7. Training a Machine Learning Model
  • Building a Recommender with TensorFlow
    • Getting Started
    • Starting a New Notebook Session
    • TensorFlow Training
  • Deploying a TensorFlow Training Job
  • Distributed Training
    • Using GPUs
    • Using Other Frameworks for Distributed Training
  • Training a Model Using Scikit-Learn
    • Starting a New Notebook Session
    • Data Preparation
    • Scikit-Learn Training
    • Explaining the Model
    • Exporting Model
    • Integration into Pipelines
  • Conclusion
• 8. Model Inference
  • Model Serving
    • Model Serving Requirements
  • Model Monitoring
    • Model Accuracy, Drift, and Explainability
    • Model Monitoring Requirements
  • Model Updating
    • Model Updating Requirements
  • Summary of Inference Requirements
  • Model Inference in Kubeflow
  • TensorFlow Serving
    • Review
      • Model serving
      • Model monitoring
      • Model updating
      • Summary
  • Seldon Core
    • Designing a Seldon Inference Graph
      • Setting up Seldon Core
      • Packaging your model
      • Creating a SeldonDeployment
    • Testing Your Model
      • Python client for Python language wrapped models
      • Local testing with Docker
    • Serving Requests
    • Monitoring Your Models
      • Model explainability
      • Sentiment prediction model
      • US Census income predictor model example
      • Outlier and drift detection
    • Review
      • Model serving
      • Model monitoring
      • Model updating
      • Summary
  • KFServing
    • Serverless and the Service Plane
    • Data Plane
    • Example Walkthrough
      • Setting up KFServing
      • Simplicity and extensibility
      • Recommender example
    • Peeling Back the Underlying Infrastructure
      • Going layer by layer
      • Escape hatches
      • Debugging an InferenceService
      • Debugging performance
      • Knative Eventing
      • Additional features
      • API documentation
    • Review
      • Model serving
      • Model monitoring
      • Model updating
      • Summary
  • Conclusion
• 9. Case Study Using Multiple Tools
  • The Denoising CT Scans Example
    • Data Prep with Python
    • DS-SVD with Apache Spark
    • Visualization
      • Downloading DRMs
      • Recomposing the matrix into denoised images
    • The CT Scan Denoising Pipeline
      • Spark operation manifest
      • The pipeline
  • Sharing the Pipeline
  • Conclusion
• 10. Hyperparameter Tuning and Automated Machine Learning
  • AutoML: An Overview
  • Hyperparameter Tuning with Kubeflow Katib
  • Katib Concepts
  • Installing Katib
  • Running Your First Katib Experiment
    • Prepping Your Training Code
    • Configuring an Experiment
    • Running the Experiment
    • Katib User Interface
  • Tuning Distributed Training Jobs
  • Neural Architecture Search
  • Advantages of Katib over Other Frameworks
  • Conclusion
• A. Argo Executor Configurations and Trade-Offs
• B. Cloud-Specific Tools and Configuration
  • Google Cloud
    • TPU-Accelerated Instances
    • Dataflow for TFX
• C. Using Model Serving in Applications
  • Building Streaming Applications Leveraging Model Serving
    • Stream Processing Engines and Libraries
    • Introducing Cloudflow
  • Building Batch Applications Leveraging Model Serving
• Index

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