Machine Learning Production Systems 1st Edition by Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu ISBN 9781098156015 by Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu instant download after payment.

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ISBN 13: 9781098156015
Author: Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu

Using machine learning for products, services, and critical business processes is quite different from using ML in an academic or research setting—especially for recent ML graduates and those moving from research to a commercial environment. Whether you currently work to create products and services that use ML, or would like to in the future, this practical book gives you a broad view of the entire field. Authors Robert Crowe, Hannes Hapke, Emily Caveness, and Di Zhu help you identify topics that you can dive into deeper, along with reference materials and tutorials that teach you the details. You'll learn the state of the art of machine learning engineering, including a wide range of topics such as modeling, deployment, and MLOps. You'll learn the basics and advanced aspects to understand the production ML lifecycle. This book provides four in-depth sections that cover all aspects of machine learning engineering: Data: collecting, labeling, validating, automation, and data preprocessing; data feature engineering and selection; data journey and storage Modeling: high performance modeling; model resource management techniques; model analysis and interoperability; neural architecture search Deployment: model serving patterns and infrastructure for ML models and LLMs; management and delivery; monitoring and logging Productionalizing: ML pipelines; classifying unstructured texts and images; genAI model pipelines

Machine Learning Production Systems 1st Table of contents:

1. Introduction to Machine Learning Production Systems
What Is Production Machine Learning?
Benefits of Machine Learning Pipelines
Focus on Developing New Models, Not on Maintaining Existing Models
Prevention of Bugs
Creation of Records for Debugging and Reproducing Results
Standardization
The Business Case for ML Pipelines
When to Use Machine Learning Pipelines
Steps in a Machine Learning Pipeline
Data Ingestion and Data Versioning
Data Validation
Feature Engineering
Model Training and Model Tuning
Model Analysis
Model Deployment
Looking Ahead
2. Collecting, Labeling, and Validating Data
Important Considerations in Data Collection
Responsible Data Collection
Labeling Data: Data Changes and Drift in Production ML
Labeling Data: Direct Labeling and Human Labeling
Validating Data: Detecting Data Issues
Validating Data: TensorFlow Data Validation
Skew Detection with TFDV
Types of Skew
Example: Spotting Imbalanced Datasets with TensorFlow Data Validation
Conclusion
3. Feature Engineering and Feature Selection
Introduction to Feature Engineering
Preprocessing Operations
Feature Engineering Techniques
Normalizing and Standardizing
Bucketizing
Feature Crosses
Dimensionality and Embeddings
Visualization
Feature Transformation at Scale
Choose a Framework That Scales Well
Avoid Training–Serving Skew
Consider Instance-Level Versus Full-Pass Transformations
Using TensorFlow Transform
Analyzers
Code Example
Feature Selection
Feature Spaces
Feature Selection Overview
Filter Methods
Wrapper Methods
Embedded Methods
Feature and Example Selection for LLMs and GenAI
Example: Using TF Transform to Tokenize Text
Benefits of Using TF Transform
Alternatives to TF Transform
Conclusion
4. Data Journey and Data Storage
Data Journey
ML Metadata
Using a Schema
Schema Development
Schema Environments
Changes Across Datasets
Enterprise Data Storage
Feature Stores
Data Warehouses
Data Lakes
Conclusion
5. Advanced Labeling, Augmentation, and Data Preprocessing
Advanced Labeling
Semi-Supervised Labeling
Active Learning
Weak Supervision
Advanced Labeling Review
Data Augmentation
Example: CIFAR-10
Other Augmentation Techniques
Data Augmentation Review
Preprocessing Time Series Data: An Example
Windowing
Sampling
Conclusion
6. Model Resource Management Techniques
Dimensionality Reduction: Dimensionality Effect on Performance
Example: Word Embedding Using Keras
Curse of Dimensionality
Adding Dimensions Increases Feature Space Volume
Dimensionality Reduction
Quantization and Pruning
Mobile, IoT, Edge, and Similar Use Cases
Quantization
Optimizing Your TensorFlow Model with TF Lite
Optimization Options
Pruning
Knowledge Distillation
Teacher and Student Networks
Knowledge Distillation Techniques
TMKD: Distilling Knowledge for a Q&A Task
Increasing Robustness by Distilling EfficientNets
Conclusion
7. High-Performance Modeling
Distributed Training
Data Parallelism
Efficient Input Pipelines
Input Pipeline Basics
Input Pipeline Patterns: Improving Efficiency
Optimizing Your Input Pipeline with TensorFlow Data
Training Large Models: The Rise of Giant Neural Nets and Parallelism
Potential Solutions and Their Shortcomings
Pipeline Parallelism to the Rescue?
Conclusion
8. Model Analysis
Analyzing Model Performance
Black-Box Evaluation
Performance Metrics and Optimization Objectives
Advanced Model Analysis
TensorFlow Model Analysis
The Learning Interpretability Tool
Advanced Model Debugging
Benchmark Models
Sensitivity Analysis
Residual Analysis
Model Remediation
Discrimination Remediation
Fairness
Fairness Evaluation
Fairness Considerations
Continuous Evaluation and Monitoring
Conclusion
9. Interpretability
Explainable AI
Model Interpretation Methods
Method Categories
Intrinsically Interpretable Models
Model-Agnostic Methods
Local Interpretable Model-Agnostic Explanations
Shapley Values
The SHAP Library
Testing Concept Activation Vectors
AI Explanations
Example: Exploring Model Sensitivity with SHAP
Regression Models
Natural Language Processing Models
Conclusion
10. Neural Architecture Search
Hyperparameter Tuning
Introduction to AutoML
Key Components of NAS
Search Spaces
Search Strategies
Performance Estimation Strategies
AutoML in the Cloud
Amazon SageMaker Autopilot
Microsoft Azure Automated Machine Learning
Google Cloud AutoML
Using AutoML
Generative AI and AutoML
Conclusion
11. Introduction to Model Serving
Model Training
Model Prediction
Latency
Throughput
Cost
Resources and Requirements for Serving Models
Cost and Complexity
Accelerators
Feeding the Beast
Model Deployments
Data Center Deployments
Mobile and Distributed Deployments
Model Servers
Managed Services
Conclusion
12. Model Serving Patterns
Batch Inference
Batch Throughput
Batch Inference Use Cases
ETL for Distributed Batch and Stream Processing Systems
Introduction to Real-Time Inference
Synchronous Delivery of Real-Time Predictions
Asynchronous Delivery of Real-Time Predictions
Optimizing Real-Time Inference
Real-Time Inference Use Cases
Serving Model Ensembles
Ensemble Topologies
Example Ensemble
Ensemble Serving Considerations
Model Routers: Ensembles in GenAI
Data Preprocessing and Postprocessing in Real Time
Training Transformations Versus Serving Transformations
Windowing
Options for Preprocessing
Enter TensorFlow Transform
Postprocessing
Inference at the Edge and at the Browser
Challenges
Model Deployments via Containers
Training on the Device
Federated Learning
Runtime Interoperability
Inference in Web Browsers
Conclusion
13. Model Serving Infrastructure
Model Servers
TensorFlow Serving
NVIDIA Triton Inference Server
TorchServe
Building Scalable Infrastructure
Containerization
Traditional Deployment Era
Virtualized Deployment Era
Container Deployment Era
The Docker Containerization Framework
Container Orchestration
Reliability and Availability Through Redundancy
Observability
High Availability
Automated Deployments
Hardware Accelerators
GPUs
TPUs
Conclusion

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Tags: Robert Crowe, Hannes Hapke, Emily Caveness, Di Zhu, Production, Systems