Machine Learning on Kubernetes: A practical handbook for building and using a complete open source machine learning platform on Kubernetes

Cover
Copyright
Contributors
Table of Contents
Preface
Part 1: The Challenges of Adopting ML and Understanding MLOps (What and Why)
Chapter 1: Challenges in Machine Learning
	Understanding ML
	Delivering ML value
	Choosing the right approach
		The importance of data
	Facing the challenges of adopting ML
		Focusing on the big picture
		Breaking down silos
		Fail-fast culture
	An overview of the ML platform
	Summary
	Further reading
Chapter 2: Understanding MLOps
	Comparing ML to traditional programming
	Exploring the benefits of DevOps
	Understanding MLOps
		ML
		DevOps
		ML project life cycle
		Fast feedback loop
		Collaborating over the project life cycle
	The role of OSS in ML projects
	Running ML projects on Kubernetes
	Summary
	Further reading
Chapter 3: Exploring Kubernetes
	Technical requirements
	Exploring Kubernetes major components
		Control plane
		Worker nodes
		Kubernetes objects required to run an application
	Becoming cloud-agnostic through Kubernetes
	Understanding Operators
	Setting up your local Kubernetes environment
		Installing kubectl
		Installing minikube
		Installing OLM
	Provisioning a VM on GCP
	Summary
Part 2: The Building Blocks of an MLOps Platform and How to Build One on Kubernetes
Chapter 4: The Anatomy of a Machine Learning Platform
	Technical requirements
	Defining a self-service platform
	Exploring the data engineering components
		Data engineer workflow
	Exploring the model development components
		Understanding the data scientist workflow
	Security, monitoring, and automation
	Introducing ODH
		Installing the ODH operator on Kubernetes
		Enabling the ingress controller on the Kubernetes cluster
		Installing Keycloak on Kubernetes
	Summary
	Further reading
Chapter 5: Data Engineering
	Technical requirements
	Configuring Keycloak for authentication
		Importing the Keycloak configuration for the ODH components
		Creating a Keycloak user
	Configuring ODH components
		Installing ODH
	Understanding and using JupyterHub
		Validating the JupyterHub installation
		Running your first Jupyter notebook
	Understanding the basics of Apache Spark
		Understanding Apache Spark job execution
	Understanding how ODH provisions Apache Spark cluster on-demand
		Creating a Spark cluster
		Understanding how JupyterHub creates a Spark cluster
	Writing and running a Spark application from Jupyter Notebook
	Summary
Chapter 6: Machine Learning Engineering
	Technical requirements
	Understanding ML engineering
	Using a custom notebook image
		Building a custom notebook container image
	Introducing MLflow
		Understanding MLflow components
		Validating the MLflow installation
	Using MLFlow as an experiment tracking system
		Adding custom data to the experiment run
	Using MLFlow as a model registry system
	Summary
Chapter 7: Model Deployment and Automation
	Technical requirements
	Understanding model inferencing with Seldon Core
		Wrapping the model using Python
		Containerizing the model
		Deploying the model using the Seldon controller
	Packaging, running, and monitoring a model using Seldon Core
	Introducing Apache Airflow
		Understanding DAG
		Exploring Airflow features
		Understanding Airflow components
		Validating the Airflow installation
		Configuring the Airflow DAG repository
		Configuring Airflow runtime images
	Automating ML model deployments in Airflow
		Creating the pipeline by using the pipeline editor
	Summary
Part 3: How to Use the MLOps Platform and Build a Full End-to-End Project Using the New Platform
Chapter 8: Building a Complete ML Project Using the Platform
	Reviewing the complete picture of the ML platform
	Understanding the business problem
	Data collection, processing, and cleaning
		Understanding data sources, location, and the format
		Understanding data processing and cleaning
	Performing exploratory data analysis
		Understanding sample data
	Understanding feature engineering
		Data augmentation
	Building and evaluating the ML model
		Selecting evaluation criteria
		Building the model
		Deploying the model
	Reproducibility
	Summary
Chapter 9: Building Your Data Pipeline
	Technical requirements
	Automated provisioning of a Spark cluster for development
	Writing a Spark data pipeline
		Preparing the environment
		Understanding data
		Designing and building the pipeline
		Using the Spark UI to monitor your data pipeline
	Building and executing a data pipeline using Airflow
		Understanding the data pipeline DAG
		Building and running the DAG
	Summary
Chapter 10: Building, Deploying, and Monitoring Your Model
	Technical requirements
	Visualizing and exploring data using JupyterHub
	Building and tuning your model using JupyterHub
	Tracking model experiments and versioning using MLflow
		Tracking model experiments
		Versioning models
	Deploying the model as a service
		Calling your model
	Monitoring your model
		Understanding monitoring components
		Configuring Grafana and a dashboard
	Summary
Chapter 11: Machine Learning on Kubernetes
	Identifying ML platform use cases
		Considering AutoML
		Commercial platforms
		ODH
	Operationalizing ML
		Setting the business expectations
		Dealing with dirty real-world data
		Dealing with incorrect results
		Maintaining continuous delivery
		Managing security
		Adhering to compliance policies
		Applying governance
	Running on Kubernetes
		Avoiding vendor lock-ins
		Considering other Kubernetes platforms
	Roadmap
	Summary
	Further reading
Index
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