book

Kubeflow for Machine Learning

by Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, Ilan Filonenko

October 2020

Intermediate to advanced

261 pages

6h 19m

English

O'Reilly Media, Inc.

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Our Assumption About YouYour Responsibility as a PractitionerConventions Used in This BookCode ExamplesUsing Code ExamplesO’Reilly Online LearningHow to Contact the AuthorsHow to Contact UsAcknowledgmentsGrievances
Model Development Life CycleWhere Does Kubeflow Fit In?Why Containerize?Why Kubernetes?Kubeflow’s Design and Core ComponentsData Exploration with NotebooksData/Feature PreparationTrainingHyperparameter TuningModel ValidationInference/PredictionPipelinesComponent OverviewAlternatives to KubeflowClipper (RiseLabs)MLflow (Databricks)OthersIntroducing Our Case StudiesModified National Institute of Standards and TechnologyMailing List DataProduct RecommenderCT ScansConclusion
Getting Set Up with KubeflowInstalling Kubeflow and Its DependenciesSetting Up Local KubernetesSetting Up Your Kubeflow Development EnvironmentCreating Our First Kubeflow ProjectTraining and Deploying a ModelTraining and Monitoring ProgressTest QueryGoing Beyond a Local DeploymentConclusion
Getting Around the Central DashboardNotebooks (JupyterHub)Training OperatorsKubeflow PipelinesHyperparameter TuningModel InferenceMetadataComponent SummarySupport ComponentsMinIOIstioKnativeApache SparkKubeflow Multiuser IsolationConclusion
Getting Started with PipelinesExploring the Prepackaged Sample PipelinesBuilding a Simple Pipeline in PythonStoring Data Between StepsIntroduction to Kubeflow Pipelines ComponentsArgo: the Foundation of PipelinesWhat Kubeflow Pipelines Adds to Argo WorkflowBuilding a Pipeline Using Existing ImagesKubeflow Pipeline ComponentsAdvanced Topics in PipelinesConditional Execution of Pipeline StagesRunning Pipelines on ScheduleConclusion
Deciding on the Correct ToolingLocal Data and Feature PreparationFetching the DataData Cleaning: Filtering Out the JunkFormatting the DataFeature PreparationCustom ContainersDistributed ToolingTensorFlow ExtendedDistributed Data Using Apache SparkDistributed Feature Preparation Using Apache SparkPutting It Together in a PipelineUsing an Entire Notebook as a Data Preparation Pipeline StageConclusion
Kubeflow ML MetadataProgrammatic QueryKubeflow Metadata UIUsing MLflow’s Metadata Tools with KubeflowCreating and Deploying an MLflow Tracking ServerLogging Data on RunsUsing the MLflow UIConclusion
Building a Recommender with TensorFlowGetting StartedStarting a New Notebook SessionTensorFlow TrainingDeploying a TensorFlow Training JobDistributed TrainingUsing GPUsUsing Other Frameworks for Distributed TrainingTraining a Model Using Scikit-LearnStarting a New Notebook SessionData PreparationScikit-Learn TrainingExplaining the ModelExporting ModelIntegration into PipelinesConclusion
Model ServingModel Serving RequirementsModel MonitoringModel Accuracy, Drift, and ExplainabilityModel Monitoring RequirementsModel UpdatingModel Updating RequirementsSummary of Inference RequirementsModel Inference in KubeflowTensorFlow ServingReviewSeldon CoreDesigning a Seldon Inference GraphTesting Your ModelServing RequestsMonitoring Your ModelsReviewKFServingServerless and the Service PlaneData PlaneExample WalkthroughPeeling Back the Underlying InfrastructureReviewConclusion

The Denoising CT Scans ExampleData Prep with PythonDS-SVD with Apache SparkVisualizationThe CT Scan Denoising PipelineSharing the PipelineConclusion
AutoML: An OverviewHyperparameter Tuning with Kubeflow KatibKatib ConceptsInstalling KatibRunning Your First Katib ExperimentPrepping Your Training CodeConfiguring an ExperimentRunning the ExperimentKatib User InterfaceTuning Distributed Training JobsNeural Architecture SearchAdvantages of Katib over Other FrameworksConclusion
Google CloudTPU-Accelerated InstancesDataflow for TFX
Building Streaming Applications Leveraging Model ServingStream Processing Engines and LibrariesIntroducing CloudflowBuilding Batch Applications Leveraging Model Serving

Content preview from Kubeflow for Machine Learning

Foreword

Occasionally over the years people will ask me what skills are most in demand in tech. Ten years ago I would tell them to study machine learning, which can scale automated decision making in ways previously impossible. However, these days I have a different answer: machine learning engineering.

Even just a few years ago if you knew machine learning and started at an organization, you would likely walk in the door as the only person with that skill set, allowing you to have an outsized impact. However, a side effect of the proliferation of books, tutorials, e-courses, and boot camps (some of which I have written myself) teaching an entire generation of technologists the skills required is that now machine learning is being used across tens of thousands of companies and organizations.

These days a more likely scenario is that, walking into your new job, you find an organization using machine learning locally but unable to deploy it to production or able to deploy models but unable to manage them effectively. In this setting, the most valuable skill is not being able to train a model, but rather to manage all those models and deploy them in ways that maximize their impact.

In this volume, Trevor Grant, Holden Karau, Boris Lublinsky, Richard Liu, and Ilan Filonenko have put together what I believe is an important cornerstone in the education of data scientists and machine learning engineers. For the foreseeable future the open source Kubeflow project will be a common tool ...