دانلود کتاب Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

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Cover
Title page
Copyright and Credits
Contributors
Preface
Section 1: What Is ML Engineering?
Chapter 1: Introduction to ML Engineering
Technical requirements
Defining a taxonomy of data disciplines
Data scientist
ML engineer
Data engineer
Assembling your team
ML engineering in the real world
What does an ML solution look like?
Why Python?
High-level ML system design
Example 1: Batch anomaly detection service
Example 2: Forecasting API
Example 3: Streamed classification
Summary
Chapter 2: The Machine Learning Development Process
Technical requirements
Setting up our tools
Setting up an AWS account
Concept to solution in four steps
Discover
Play
Develop
Deploy
Summary
Section 2: ML Development and Deployment
Chapter 3: From Model to Model Factory
Technical requirements
Defining the model factory
Designing your training system
Training system design options
Train-run
Train-persist
Retraining required
Detecting drift
Engineering features for consumption
Engineering categorical features
Engineering numerical features
Learning about learning
Defining the target
Cutting your losses
Hierarchies of automation
Optimizing hyperparameters
AutoML
Auto-sklearn
Persisting your models
Building the model factory with pipelines
Scikit-learn pipelines
Spark ML pipelines
Summary
Chapter 4: Packaging Up
Technical Requirements
Writing good Python
Recapping the basics
Tips and tricks
Adhering to standards
Writing good PySpark
Choosing a style
Object-oriented programming
Functional programming
Packaging your code
Why package?
Selecting use cases for packaging
Designing your package
Building your package
Testing, logging, and error handling
Testing
Logging
Error handling
Not reinventing the wheel
Summary
Chapter 5: Deployment Patterns and Tools
Technical requirements
Architecting systems
Exploring the unreasonable effectiveness of patterns
Swimming in data lakes
Microservices
Event-based designs
Batching
Containerizing
Hosting your own microservice on AWS
Pushing to ECR
Hosting on ECS
Creating a load balancer
Pipelining 2.0
Revisiting CI/CD
Summary
Chapter 6: Scaling Up
Technical Requirements
Scaling with Spark
Spark tips and tricks
Spark on the cloud
Spinning up serverless infrastructure
Containerizing at scale with Kubernetes
Summary
Section 3: End-to-End Examples
Chapter 7: Building an Example ML Microservice
Technical Requirements
Understanding the forecasting problem
Designing our forecasting service
Selecting the tools
Executing the build
Training pipeline and forecaster
Training and forecast handlers
Summary
Chapter 8: Building an Extract Transform Machine Learning Use Case
Technical Requirements
Understanding the batch processing problem
Designing an ETML solution
Selecting the tools
Interfaces
Scaling of models
Scheduling of ETML pipelines
Executing the build
Not reinventing the wheel in practice
Using the Gitflow workflow
Injecting some engineering practices
About Packt
Other Books You May Enjoy
Index