Kubeflow for Machine Learning

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Autorzy:: Trevor Grant, Holden Karau, Boris Lublinsky

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If you're training a machine learning model but aren't sure how to put it into production, this book will get you there. Kubeflow provides a collection of cloud native tools for different stages of a model's lifecycle, from data exploration, feature preparation, and model training to model serving. This guide helps data scientists build production-grade machine learning implementations with Kubeflow and shows data engineers how to make models scalable and reliable.

Using examples throughout the book, authors Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky explain how to use Kubeflow to train and serve your machine learning models on top of Kubernetes in the cloud or in a development environment on-premises.

Understand Kubeflow's design, core components, and the problems it solves
Understand the differences between Kubeflow on different cluster types
Train models using Kubeflow with popular tools including Scikit-learn, TensorFlow, and Apache Spark
Keep your model up to date with Kubeflow Pipelines
Understand how to capture model training metadata
Explore how to extend Kubeflow with additional open source tools
Use hyperparameter tuning for training
Learn how to serve your model in production

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O autorze książki

Holden Karau is a software development engineer and is active in the open source. She has worked on a variety of search, classification, and distributed systems problems at IBM, Alpine, Databricks, Google, Foursquare, and Amazon. She graduated from the University of Waterloo with a bachelor's of mathematics degree in computer science. Other than software, she enjoys playing with fire and hula hoops, and welding.

Trevor Grant, Holden Karau, Boris Lublinsky - pozostałe książki

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Oceny i opinie klientów: Kubeflow for Machine Learning Trevor Grant, Holden Karau, Boris Lublinsky (0) Weryfikacja opinii następuję na podstawie historii zamówień na koncie Użytkownika umieszczającego opinię. Użytkownik mógł otrzymać punkty za opublikowanie opinii uprawniające do uzyskania rabatu w ramach Programu Punktowego.

Szczegóły książki

ISBN Ebooka:: 978-14-920-5007-0, 9781492050070
Data wydania ebooka :: 2020-10-13 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: 1
Rozmiar pliku ePub:: 10.6MB
Rozmiar pliku Mobi:: 22MB

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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