The Machine Learning Engineer's Path
Build a Production-Ready Recommender System with PythonBy Priya Quill
Length7h 34m
About this audiobook
machine learning engineer production machine learning Build, train, and deploy production models with Python, scikit-learn, and PyTorch, then ship them as live, monitored services. This practical, hands-on guide covers the entire ML lifecycle—from data preparation and model development to deployment, monitoring, and maintenance. Whether you're a beginner or a seasoned pro, you'll learn how to create robust, scalable systems that deliver real business value. Priya Quill walks you through each stage with clear examples and best practices, ensuring you can transition from notebook experiments to production-grade applications. Topics include: setting up your environment, feature engineering, model selection, hyperparameter tuning, containerization with Docker, orchestration with Kubernetes, CI/CD pipelines, A/B testing, logging, alerting, and performance optimization. By the end, you'll have the skills to build and manage end-to-end machine learning systems that are reliable, maintainable, and ready for the real world. Unlike other resources that focus only on theory, this book emphasizes practical implementation and operational excellence. For readers who enjoyed [placeholder] and [placeholder], this is your next step.
This hands-on AI/ML/Data guide is written to be used at the keyboard: every concept is paired with something you can run, adapt, and keep. You move from first principles to real, working results, with the common errors and fixes called out along the way so you are never stuck for long.
Audiobook details
GenreTechnology
Length7 hrs 34 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
LanguageEnglish
Table of contents
1Introduction
2Preface
3Chapter 1 — Project Kickoff: Define the Problem and Set Up Environment
4Chapter 2 — Data Wrangling: Preprocess and Clean Raw Ratings and Movie Metadata
5Chapter 3 — Exploratory Data Analysis: Understand User Behavior and Movie Attributes
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6Chapter 4 — Feature Engineering: Create User and Item Profiles
7Chapter 5 — Baseline Models: Implement Popularity and Simple Content-Based Filtering
8Chapter 6 — Collaborative Filtering: User-Based and Item-Based Neighborhood Methods
9Chapter 7 — Matrix Factorization: SVD and Alternating Least Squares (ALS)
10Chapter 8 — Model Evaluation and Validation: RMSE, Precision@K, Ranking Metrics
11Chapter 9 — Hyperparameter Tuning: Grid Search and Bayesian Optimization
12Chapter 10 — Deep Learning Foundations for Recommenders: Neural Networks with TensorFlow
13Chapter 11 — Building a Neural Collaborative Filtering (NCF) Model
14Chapter 12 — Cold-Start Problem: Handling New Users and Items with Hybrid Approaches
15Chapter 13 — Production Pipelines: Automating Data Ingestion and Model Training
16Chapter 14 — Model Serialization and Versioning with MLflow
17Chapter 15 — Building a REST API with Flask/FastAPI
18Chapter 16 — Containerizing with Docker and Orchestrating with Kubernetes
19Chapter 17 — Deploying to Cloud (AWS SageMaker or GCP AI Platform)
20Chapter 18 — Monitoring, Logging, and Continuous Improvement
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