Machine Learning with LightGBM and Python : A Practitioner's Guide to Developing Production-Ready Machine Learning Systems / Andrich van Wyk.

Format:

Book

Author/Creator:

Wyk, Andrich van, .

Language:

English

Subjects (All):

Machine learning.

Python (Computer program language).

Data mining.

Physical Description:

1 online resource (252 p.)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing Ltd., [2023]

Biography/History:

Wyk Andrich van: Andrich van Wyk has 15 years of experience in machine learning R&D and building AI-driven solutions. He also has broad experience as a software engineer and architect with over a decade of industry experience working on enterprise systems. He graduated cum laude with an M. Sc. in Computer Science from the University of Pretoria. His work focused on neural networks and population-based algorithms such as Particle Swarm Optimization and Honey-Bee Foraging. Andrich also writes about software and machine learning on his blog and his Substack. He currently resides in South Africa with his wife and daughter.

Summary:

Take your software to the next level and solve real-world data science problems by building production-ready machine learning solutions using LightGBM and Python Key Features Get started with LightGBM, a powerful gradient-boosting library for building ML solutions Apply data science processes to real-world problems through case studies Elevate your software by building machine learning solutions on scalable platforms Purchase of the print or Kindle book includes a free PDF eBook Book Description Machine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems that are ready for release. This book will get you acquainted with the high-performance gradient-boosting LightGBM framework and show you how it can be used to solve various machine-learning problems to produce highly accurate, robust, and predictive solutions. Starting with simple machine learning models in scikit-learn, you'll explore the intricacies of gradient boosting machines and LightGBM. You'll be guided through various case studies to better understand the data science processes and learn how to practically apply your skills to real-world problems. As you progress, you'll elevate your software engineering skills by learning how to build and integrate scalable machine-learning pipelines to process data, train models, and deploy them to serve secure APIs using Python tools such as FastAPI. By the end of this book, you'll be well equipped to use various -of-the-art tools that will help you build production-ready systems, including FLAML for AutoML, PostgresML for operating ML pipelines using Postgres, high-performance distributed training and serving via Dask, and creating and running models in the Cloud with AWS Sagemaker. What you will learn Get an overview of ML and working with data and models in Python using scikit-learn Explore decision trees, ensemble learning, gradient boosting, DART, and GOSS Master LightGBM and apply it to classification and regression problems Tune and train your models using AutoML with FLAML and Optuna Build ML pipelines in Python to train and deploy models with secure and performant APIs Scale your solutions to production readiness with AWS Sagemaker, PostgresML, and Dask Who this book is for This book is for software engineers aspiring to be better machine learning engineers and data scientists unfamiliar with LightGBM, looking to gain in-depth knowledge of its libraries. Basic to intermediate Python programming knowledge is required to get started with the book. The book is also an excellent source for ML veterans, with a strong focus on ML engineering with up-to-date and thorough coverage of platforms such as AWS Sagemaker, PostgresML, and Dask.

Contents:

Cover

Copyright

Contributors

Table of Contents

Preface

Part 1:Gradient Boosting and LightGBM Fundamentals

Chapter 1: Introducing Machine Learning

Technical requirements

What is machine learning?

Machine learning paradigms

Introducing models, datasets, and supervised learning

Models

Hyperparameters

Datasets

Overfitting and generalization

Supervised learning

Model performance metrics

A modeling example

Decision tree learning

Entropy and information gain

Building a decision tree using C4.5

Overfitting in decision trees

Building decision trees with scikit-learn

Decision tree hyperparameters

Summary

References

Chapter 2: Ensemble Learning - Bagging and Boosting

Ensemble learning

Bagging and random forests

Random forest

Gradient-boosted decision trees

Gradient descent

Gradient boosting

Gradient-boosted decision tree hyperparameters

Gradient boosting in scikit-learn

Advanced boosting algorithm - DART

Chapter 3: An Overview of LightGBM in Python

Introducing LightGBM

LightGBM optimizations

Limitations of LightGBM

Getting started with LightGBM in Python

LightGBM Python API

LightGBM scikit-learn API

Building LightGBM models

Cross-validation

Parameter optimization

Predicting student academic success

Chapter 4: Comparing LightGBM, XGBoost, and Deep Learning

An overview of XGBoost

Comparing XGBoost and LightGBM

Python XGBoost example

Deep learning and TabTransformers

What is deep learning?

Introducing TabTransformers

Comparing LightGBM, XGBoost, and TabTransformers

Predicting census income

Detecting credit card fraud

References.

Part 2:Practical Machine Learning with LightGBM

Chapter 5: LightGBM Parameter Optimization with Optuna

Optuna and optimization algorithms

Introducing Optuna

Optimization algorithms

Pruning strategies

Optimizing LightGBM with Optuna

Advanced Optuna features

Chapter 6: Solving Real-World Data Science Problems with LightGBM

The data science life cycle

Defining the data science life cycle

Predicting wind turbine power generation with LightGBM

Problem definition

Data collection

Data preparation

EDA

Modeling

Model deployment

Communicating results

Classifying individual credit scores with LightGBM

Model deployment and results

Chapter 7: AutoML with LightGBM and FLAML

Automated machine learning

Automating feature engineering

Automating model selection and tuning

Risks of using AutoML systems

Introducing FLAML

Cost Frugal Optimization

BlendSearch

FLAML limitations

Case study - using FLAML with LightGBM

Feature engineering

FLAML AutoML

Zero-shot AutoML

Part 3:Production-ready Machine Learning with LightGBM

Chapter 8: Machine Learning Pipelines and MLOps with LightGBM

Introducing machine learning pipelines

Scikit-learn pipelines

Understanding MLOps

Deploying an ML pipeline for customer churn

Building an ML pipeline using scikit-learn

Building an ML API using FastAPI

Containerizing our API

Deploying LightGBM to Google Cloud

Chapter 9: LightGBM MLOps with AWS SageMaker

An introduction to AWS and SageMaker

AWS

SageMaker.

SageMaker Clarify

Building a LightGBM ML pipeline with Amazon SageMaker

Setting up a SageMaker session

Preprocessing step

Model training and tuning

Evaluation, bias, and explainability

Deploying and monitoring the LightGBM model

Results

Chapter 10: LightGBM Models with PostgresML

Introducing PostgresML

Latency and round trips

Getting started with PostgresML

Training models

Deploying and prediction

PostgresML dashboard

Case study - customer churn with PostgresML

Data loading and preprocessing

Training and hyperparameter optimization

Predictions

Chapter 11: Distributed and GPU-Based Learning with LightGBM

Distributed learning with LightGBM and Dask

GPU training for LightGBM

Setting up LightGBM for the GPU

Running LightGBM on the GPU

Index

Other Books You May Enjoy.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on print version record.

Other Format:

Erscheint auch als

ISBN:

9781800563056

1800563051

OCLC:

1446511367