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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Preface
Who This Book Is ForNavigating This BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgmentsYaronNoah
1. MLOps: What Is It and Why Do We Need It?
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
2. The Stages of MLOps
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
3. Getting Started with Your First MLOps Project
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
4. Working with Data and Feature Stores
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
5. Developing Models for Production
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
6. Deployment of Models and AI Applications
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
7. Building a Production Grade MLOps Project from A to Z
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
8. Building Scalable Deep Learning and Large Language Model Projects
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
9. Solutions for Advanced Data Types
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

10. Implementing MLOps Using Rust
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
A. Job Interview Questions
B. Enterprise MLOps Interviews
Index
About the Authors

Content preview from Implementing MLOps in the Enterprise

Appendix A. Job Interview Questions

The following section contains questions and answers that may arise during a job interview. Python for DevOps (O’Reilly), by Noah Gift, Kennedy Behrman, Alfredo Deza, and Grig Gheorghiu, covers these topics in more detail.

What is the primary purpose of DevOps?

The primary purpose of DevOps is to increase the speed and quality of software development while also reducing costs. DevOps combines software development (Dev) and information technology operations (Ops) to shorten the time it takes to deliver customer features and updates.

What is an excellent example of the fundamental processes necessary to implement MLOps?

An excellent example of the fundamental processes necessary to implement MLOps would be establishing a clear understanding of your organization’s goals for using machine learning. Once you understand, you can develop the strategies and infrastructure to support your ML models. This process can include setting up a data pipeline, establishing a model training and testing process, and deploying models into a production environment.

What is a feature store?

A feature store is a database that stores the features machine learning models can use. A feature store stores features extracted from data sources and components generated by feature engineering processes.

What is a Model Registry?

A Model Registry is a database that stores machine learning models: a Model Registry stores models trained on data and models generated by model ...

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Implementing MLOps in the Enterprise

by Yaron Haviv, Noah Gift