book

Implementing MLOps in the Enterprise

by Yaron Haviv, Noah Gift

December 2023

Intermediate to advanced

377 pages

9h 21m

English

O'Reilly Media, Inc.

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Who This Book Is ForNavigating This BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgmentsYaronNoah
What Is MLOps?MLOps in the EnterpriseUnderstanding ROI in Enterprise SolutionsUnderstanding Risk and Uncertainty in the EnterpriseMLOps Versus DevOpsWhat Isn’t MLOps?Mainstream Definitions of MLOpsWhat Is ML Engineering?MLOps and Business IncentivesMLOps in the CloudKey Cloud Development EnvironmentsThe Key Players in Cloud ComputingMLOps On-PremisesMLOps in Hybrid EnvironmentsEnterprise MLOps StrategyConclusionCritical Thinking Discussion QuestionsExercises
Getting StartedChoose Your AlgorithmDesign Your PipelinesData Collection and PreparationData Storage and IngestionData Exploration and PreparationData LabelingFeature StoresModel Development and TrainingWriting and Maintaining Production ML CodeTracking and Comparing Experiment ResultsDistributed Training and Hyperparameter OptimizationBuilding and Testing Models for ProductionDeployment (and Online ML Services)From Model Endpoints to Application PipelinesOnline Data PreparationContinuous Model and Data MonitoringMonitoring Data and Concept DriftMonitoring Model Performance and AccuracyThe Strategy of Pretrained ModelsBuilding an End-to-End Hugging Face ApplicationFlow Automation (CI/CD for ML)ConclusionCritical Thinking Discussion QuestionsExercises
Identifying the Business Use Case and GoalsFinding the AI Use CaseDefining Goals and Evaluating the ROIHow to Build a Successful ML ProjectApproving and Prototyping the ProjectScaling and Productizing ProjectsProject Structure and LifecycleML Project Example from A to ZExploratory Data AnalysisData and Model Pipeline DevelopmentApplication Pipeline DevelopmentScaling and Productizing the ProjectCI/CD and Continuous OperationsConclusionCritical Thinking Discussion QuestionsExercises
Data Versioning and LineageHow It WorksCommon ML Data Versioning ToolsData Preparation and Analysis at ScaleStructured and Unstructured Data TransformationsDistributed Data Processing ArchitecturesInteractive Data ProcessingBatch Data ProcessingStream ProcessingStream Processing FrameworksFeature StoresFeature Store Architecture and UsageIngestion and Transformation ServiceFeature StorageFeature Retrieval (for Training and Serving)Feature Stores Solutions and Usage ExampleUsing Feast Feature StoreUsing MLRun Feature StoreConclusionCritical Thinking Discussion QuestionsExercises
AutoMLRunning, Tracking, and Comparing ML JobsExperiment TrackingSaving Essential Metadata with the Model ArtifactsComparing ML Jobs: An Example with MLflowHyperparameter TuningAuto-LoggingMLOps Automation: AutoMLOpsExample: Running and Tracking ML Jobs Using Azure DatabricksHandling Training at ScaleBuilding and Running Multi-Stage WorkflowsManaging Computation Resources EfficientlyConclusionCritical Thinking Discussion QuestionsExercises
Model Registry and ManagementSolution ExamplesSageMaker ExampleMLflow ExampleMLRun ExampleModel ServingAmazon SageMakerSeldon CoreMLRun ServingAdvanced Serving and Application PipelinesImplementing Scalable Application PipelinesModel Routing and EnsemblesModel Optimization and ONNXData and Model MonitoringIntegrated Model Monitoring SolutionsStandalone Model Monitoring SolutionsModel RetrainingWhen to Retrain Your ModelsStrategies for Data RetrainingModel Retraining in the MLOps PipelineDeployment StrategiesMeasuring the Business ImpactConclusionCritical Thinking Discussion QuestionsExercises
Exploratory Data AnalysisInteractive Data PreparationPreparing the Credit Transaction DatasetPreparing the User Events (Activities) DatasetExtracting Labels and Training a ModelData Ingestion and Preparation Using a Feature StoreBuilding the Credit Transactions Data Pipeline (Feature Set)Building the User Events Data Pipeline (FeatureSet)Building the Target Labels Data Pipeline (FeatureSet)Ingesting Data into the Feature StoreModel Training and Validation PipelineCreating and Evaluating a Feature VectorBuilding and Running an Automated Training and Validation PipelineReal-Time Application PipelineDefining a Custom Model Serving ClassBuilding an Application Pipeline with Enrichment and EnsembleTesting the Application Pipeline LocallyDeploying and Testing the Real-Time Application PipelineModel MonitoringCI/CD and Continuous OperationsConclusionCritical Thinking Discussion QuestionsExercises
Distributed Deep LearningHorovodRayData Gathering, Labeling, and Monitoring in DLData Labeling Pitfalls to AvoidData Labeling Best PracticesData Labeling SolutionsUsing Foundation Models as LabelersMonitoring DL Models with Unstructured DataBuild Versus Buy Deep Learning ModelsFoundation Models, Generative AI, LLMsRisks and Challenges with Generative AIMLOps Pipelines for Efficiently Using and Customizing LLMsApplication Example: Fine-Tuning an LLM ModelConclusionCritical Thinking Discussion QuestionsExercises
ML Problem Framing with Time SeriesNavigating Time Series Analysis with AWSDiving into Time Series with DeepAR+Time Series with the GCP BigQuery and SQLBuild Versus Buy for MLOps NLP ProblemsBuild Versus Buy: The Hugging Face ApproachExploring Natural Language Processing with AWSExploring NLP with OpenAIVideo Analysis, Image Classification, and Generative AIImage Classification Techniques with CreateMLComposite AIGetting Started with Serverless for Composite AIUse Cases of Composite AI with ServerlessConclusionCritical Thinking Discussion QuestionsExercises

The Case for Rust for MLOpsLeveling Up with Rust, GitHub Copilot, and CodespacesIn the Beginning Was the Command LineGetting Started with Rust for MLOpsUsing PyTorch and Hugging Face with RustUsing Rust to Build Tools for MLOpsBuilding Containerized Rust Command-Line ToolsGPU PyTorch WorkflowsUsing TensorFlow RustDoing k-means Clustering with RustFinal Notes on RustRuff Linterrust-new-project-templateConclusionCritical Thinking Discussion QuestionsExercises

Overview

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.

Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.

You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:

Learn the MLOps process, including its technological and business value
Build and structure effective MLOps pipelines
Efficiently scale MLOps across your organization
Explore common MLOps use cases
Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI
Learn how to prepare for and adapt to the future of MLOps
Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy