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Spis treści

Implementing MLOps in the Enterprise eBook -- spis treści

• Preface
  • Who This Book Is For
  • Navigating This Book
  • Conventions Used in This Book
  • Using Code Examples
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
    • Yaron
    • Noah
• 1. MLOps: What Is It and Why Do We Need It?
  • What Is MLOps?
    • MLOps in the Enterprise
    • Understanding ROI in Enterprise Solutions
    • Understanding Risk and Uncertainty in the Enterprise
    • MLOps Versus DevOps
    • What Isnt MLOps?
  • Mainstream Definitions of MLOps
    • What Is ML Engineering?
    • MLOps and Business Incentives
  • MLOps in the Cloud
    • Key Cloud Development Environments
    • The Key Players in Cloud Computing
      • AWS view of cloud computing as it relates to MLOps
      • Azure view of cloud computing as it relates to MLOps
      • GCP view of cloud computing as it relates to MLOps
  • MLOps On-Premises
  • MLOps in Hybrid Environments
  • Enterprise MLOps Strategy
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• 2. The Stages of MLOps
  • Getting Started
    • Choose Your Algorithm
    • Design Your Pipelines
  • Data Collection and Preparation
    • Data Storage and Ingestion
    • Data Exploration and Preparation
    • Data Labeling
    • Feature Stores
  • Model Development and Training
    • Writing and Maintaining Production ML Code
    • Tracking and Comparing Experiment Results
    • Distributed Training and Hyperparameter Optimization
    • Building and Testing Models for Production
  • Deployment (and Online ML Services)
    • From Model Endpoints to Application Pipelines
    • Online Data Preparation
  • Continuous Model and Data Monitoring
    • Monitoring Data and Concept Drift
    • Monitoring Model Performance and Accuracy
  • The Strategy of Pretrained Models
  • Building an End-to-End Hugging Face Application
  • Flow Automation (CI/CD for ML)
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• 3. Getting Started with Your First MLOps Project
  • Identifying the Business Use Case and Goals
    • Finding the AI Use Case
    • Defining Goals and Evaluating the ROI
  • How to Build a Successful ML Project
    • Approving and Prototyping the Project
    • Scaling and Productizing Projects
    • Project Structure and Lifecycle
  • ML Project Example from A to Z
    • Exploratory Data Analysis
    • Data and Model Pipeline Development
    • Application Pipeline Development
      • Real-time application pipelines
      • Batch application pipelines
    • Scaling and Productizing the Project
      • Adding tests
      • ML pipelines and hyperparameter optimization
    • CI/CD and Continuous Operations
      • Continuously monitoring data and models
      • Integrating with a CI/CD service
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• 4. Working with Data and Feature Stores
  • Data Versioning and Lineage
    • How It Works
    • Common ML Data Versioning Tools
      • Data Version Control
      • Pachyderm
      • MLflow Tracking
      • MLRun
      • Other Frameworks
  • Data Preparation and Analysis at Scale
    • Structured and Unstructured Data Transformations
    • Distributed Data Processing Architectures
    • Interactive Data Processing
    • Batch Data Processing
    • Stream Processing
    • Stream Processing Frameworks
  • Feature Stores
    • Feature Store Architecture and Usage
    • Ingestion and Transformation Service
    • Feature Storage
    • Feature Retrieval (for Training and Serving)
  • Feature Stores Solutions and Usage Example
    • Using Feast Feature Store
    • Using MLRun Feature Store
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• 5. Developing Models for Production
  • AutoML
  • Running, Tracking, and Comparing ML Jobs
    • Experiment Tracking
    • Saving Essential Metadata with the Model Artifacts
    • Comparing ML Jobs: An Example with MLflow
    • Hyperparameter Tuning
    • Auto-Logging
  • MLOps Automation: AutoMLOps
    • Example: Running and Tracking ML Jobs Using Azure Databricks
    • Handling Training at Scale
    • Building and Running Multi-Stage Workflows
    • Managing Computation Resources Efficiently
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• 6. Deployment of Models and AI Applications
  • Model Registry and Management
  • Solution Examples
    • SageMaker Example
    • MLflow Example
    • MLRun Example
  • Model Serving
    • Amazon SageMaker
    • Seldon Core
    • MLRun Serving
  • Advanced Serving and Application Pipelines
    • Implementing Scalable Application Pipelines
      • AWS Step Functions
      • Apache Beam
      • MLRun serving graphs
    • Model Routing and Ensembles
    • Model Optimization and ONNX
  • Data and Model Monitoring
    • Integrated Model Monitoring Solutions
      • Amazon SageMaker
      • Google Vertex AI
      • MLRun
    • Standalone Model Monitoring Solutions
  • Model Retraining
    • When to Retrain Your Models
    • Strategies for Data Retraining
    • Model Retraining in the MLOps Pipeline
  • Deployment Strategies
  • Measuring the Business Impact
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• 7. Building a Production Grade MLOps Project from A to Z
  • Exploratory Data Analysis
  • Interactive Data Preparation
    • Preparing the Credit Transaction Dataset
    • Preparing the User Events (Activities) Dataset
    • Extracting Labels and Training a Model
  • Data Ingestion and Preparation Using a Feature Store
    • Building 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 Store
      • Batch data ingestion (for tests and training)
      • Real-time data ingestion (for production)
  • Model Training and Validation Pipeline
    • Creating and Evaluating a Feature Vector
    • Building and Running an Automated Training and Validation Pipeline
  • Real-Time Application Pipeline
    • Defining a Custom Model Serving Class
    • Building an Application Pipeline with Enrichment and Ensemble
    • Testing the Application Pipeline Locally
    • Deploying and Testing the Real-Time Application Pipeline
  • Model Monitoring
  • CI/CD and Continuous Operations
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• 8. Building Scalable Deep Learning and Large Language Model Projects
  • Distributed Deep Learning
    • Horovod
    • Ray
  • Data Gathering, Labeling, and Monitoring in DL
    • Data Labeling Pitfalls to Avoid
    • Data Labeling Best Practices
    • Data Labeling Solutions
    • Using Foundation Models as Labelers
    • Monitoring DL Models with Unstructured Data
  • Build Versus Buy Deep Learning Models
  • Foundation Models, Generative AI, LLMs
    • Risks and Challenges with Generative AI
    • MLOps Pipelines for Efficiently Using and Customizing LLMs
    • Application Example: Fine-Tuning an LLM Model
      • Data preparation and tuning
        • Data preparation
        • Model tuning and evaluation
        • Defining an MLRun project and running the pipeline
      • Application and model serving pipeline
      • Adding a web interface
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• 9. Solutions for Advanced Data Types
  • ML Problem Framing with Time Series
    • Navigating Time Series Analysis with AWS
    • Diving into Time Series with DeepAR+
    • Time Series with the GCP BigQuery and SQL
  • Build Versus Buy for MLOps NLP Problems
  • Build Versus Buy: The Hugging Face Approach
  • Exploring Natural Language Processing with AWS
  • Exploring NLP with OpenAI
  • Video Analysis, Image Classification, and Generative AI
  • Image Classification Techniques with CreateML
  • Composite AI
    • Getting Started with Serverless for Composite AI
    • Use Cases of Composite AI with Serverless
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• 10. Implementing MLOps Using Rust
  • The Case for Rust for MLOps
    • Leveling Up with Rust, GitHub Copilot, and Codespaces
    • In the Beginning Was the Command Line
  • Getting Started with Rust for MLOps
  • Using PyTorch and Hugging Face with Rust
  • Using Rust to Build Tools for MLOps
    • Building Containerized Rust Command-Line Tools
    • GPU PyTorch Workflows
    • Using TensorFlow Rust
    • Doing k-means Clustering with Rust
  • Final Notes on Rust
    • Ruff Linter
    • rust-new-project-template
  • Conclusion
  • Critical Thinking Discussion Questions
  • Exercises
• A. Job Interview Questions
  • What is the primary purpose of DevOps?
  • What is an excellent example of the fundamental processes necessary to implement MLOps?
  • What is a feature store?
  • What is a Model Registry?
  • What are the best practices for operationalizing a microservice?
  • What is GitHub Actions, and what are the primary use cases?
  • What is a data pipeline?
  • What are the primary use cases for Jupyter Notebook?
  • What is the purpose of linting Python code?
  • Why are cloud-based development environments like GitHub Codespaces and AWS Cloud9 useful?
  • What is Big O Notation?
  • What are business use cases for the mathematical field of optimization?
  • What is the traveling salesman problem?
  • Describe how the gradient descent algorithm works?
  • The greedy coin problem is what type of programming problem?
  • What are the advantages of containers?
  • What is an HTTP API?
  • What are the advantages of containerized ML applications?
  • What are the advantages of using ONNX for model interoperability?
  • What are the use cases for edge-based machine learning models?
  • What is a Spark Cluster?
  • What problems does PySpark solve?
  • What are the critical components of the Databricks platform?
  • What are the critical components of MLflow?
  • What is the critical difference between a Spark DataFrame and a pandas DataFrame?
  • What is kaizen?
  • What is a data warehouse?
  • What is a scheduled data pipeline job?
  • What is data engineering?
  • What is DataOps?
  • What is Kubernetes?
  • Why are microservices a good fit for Kubernetes?
  • What is observability in software engineering?
  • What are the critical components of Kubernetes?
  • What is the Kubernetes API?
  • What are the core components of a cloud native architecture?
  • What are three cloud native data components?
  • What are the common fallacies of distributed computing?
  • How do you access network storage in Docker?
  • What is block storage?
• B. Enterprise MLOps Interviews
  • Shubham Saboo and Sandra Kublik
  • Piero Molino
  • Asaf Somekh
  • Javier Luraschi and Pedro Luraschi
  • Malcolm Smith Fraser
  • Jon Reifschneider
  • Julien Simon
  • Shubham Saboo
  • Brian Ray
  • Simon Stebelena
  • Bindu Reddy
  • Dhanasekar Sundararaman
  • Ville Tuulos
  • Lewis Tunstall and Leandro von Werra
  • Arvs Lat
  • Julien Simon, Yaron Haviv, and Noah Gift
  • Nic Stone
  • Doris Xin
• Index