Ultimate Machine Learning with LightGBM Using TensorFlow

Boost the Model. Deepen the Intelligence. Ship to Production.

Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com.
● Hands-on LightGBM and TensorFlow mastery covering gradient boosting, neural networks, and hybrid model architectures.
● Production-ready MLOps pipelines with Docker, Kubernetes, CI/CD automation, and cloud deployment for enterprise AI.
● Explainable AI implementation using SHAP, LIME, and TensorBoard alongside ChatGPT and GitHub Copilot for accelerated development.

Book Description
Build Models That Win on Accuracy—and Systems That Win in Production.
Machine Learning (ML) practitioners who can combine gradient boosting with deep learning, and deploy the results to production are the engineers solving the hardest problems in enterprise AI. Ultimate Machine Learning with LightGBM Using TensorFlow provides a practical, end-to-end guide for building intelligent, scalable, and production-ready ML systems using two of the most powerful frameworks in modern AI.

You begin with ML fundamentals and ensemble methods, then advance through XGBoost, LightGBM, and TensorFlow neural networks before combining them into hybrid architectures that deliver performance. Each chapter integrates hands-on Python examples, feature engineering, hyperparameter tuning, explainable AI using SHAP, LIME, and TensorBoard, with AI-assisted development throughout using ChatGPT and GitHub Copilot.

The final section addresses production deployment, covering MLOps practices, Docker, Kubernetes, CI/CD pipelines, and cloud deployment. Thus, by the end of the book, you will design, optimize, deploy, and manage enterprise-grade ML applications with technical depth and practical confidence.

What you will learn
● Build high-performance ML models using LightGBM and TensorFlow for real-world business applications.
● Design hybrid AI architectures combining gradient boosting and deep learning for complex problem solving.
● Optimise model accuracy through feature engineering, hyperparameter tuning, and explainable AI methods.
● Develop production-ready ML pipelines using MLOps, Docker, Kubernetes, and automated CI/CD practices.
● Implement end-to-end ML workflows from data preparation through cloud deployment with production confidence.
● Leverage ChatGPT and GitHub Copilot to accelerate machine learning development and debugging workflows.

1. Introduction to ML Landscape
2. Ensemble Methods and Tree-Based Models
3. XGBoost and Gradient Boosting
4. Introduction to LightGBM
5. Data Preparation and Feature Engineering for LightGBM
6. Hyperparameter Tuning for Optimal Performance
7. Practical Applications and Interpretability
8. Introduction to TensorFlow
9. Crafting and Training a Neural Network
10. Efficient Data Pipelines with tf.data
11. Building Hybrid and Stacked Models
12. Seamless Integration with tf.data
13. Advanced Topics and Real-World Case Studies
14. A Final Project
15. The Future of ML and Further Learning
Index

Gaurav Singh is a Data Science Lead and AI professional specializing in Machine Learning, LLMs, Generative AI, Agentic AI, and cloud technologies. With expertise in LightGBM, TensorFlow, RAG, MLOps, and enterprise AI, he combines hands-on industry experience with practical strategies to build scalable, production-ready AI solutions.

About the Technical Reviewer

Siddhesh Lele is a Manager in Ernst & Young LLP’s Financial Crime Compliance practice, where he specializes in the intersection of artificial intelligence, machine learning, and anti-money laundering compliance. With more than a decade of experience advising global financial institutions, he brings deep expertise in AML program design, transaction monitoring model validation, and independent internal audit, with a particular focus on applying advanced analytics and machine learning techniques to financial crime risk management challenges.

Mr. Lele has led large-scale regulatory remediation programs and independent validation engagements for financial institutions operating under federal consent orders issued by the Office of the Comptroller of the Currency and the Federal Reserve. His work spans the full spectrum of financial crime compliance — from model development and behavioral analytics to enterprise-wide control assessments and compliance technology implementation — and is distinguished by his ability to combine rigorous regulatory expertise with applied machine learning capability.

A certified Anti-Money Laundering Specialist (CAMS), Mr. Lele holds a Master of Science in Information Systems from Pace University, New York. He has contributed expert reviews to academic and professional publications in the AI and Machine Learning (ML) domains.