Machine Learning Systems
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Designing Machine Learning Systems
Many tutorials show you how to develop ML systems from ideation to deployed models. But with constant changes in tooling, those systems can quickly become outdated. Without an intentional design to hold the components together, these systems will become a technical liability, prone to errors and be quick to fall apart. In this book, Chip Huyen provides a framework for designing real-world ML systems that are quick to deploy, reliable, scalable, and iterative. These systems have the capacity to learn from new data, improve on past mistakes, and adapt to changing requirements and environments. Youâ??ll learn everything from project scoping, data management, model development, deployment, and infrastructure to team structure and business analysis. Learn the challenges and requirements of an ML system in production Build training data with different sampling and labeling methods Leverage best techniques to engineer features for your ML models to avoid data leakage Select, develop, debug, and evaluate ML models that are best suit for your tasks Deploy different types of ML systems for different hardware Explore major infrastructural choices and hardware designs Understand the human side of ML, including integrating ML into business, user experience, and team structure.
Machine Learning System Design
Author: Valerii Babuskhin
language: en
Publisher: Simon and Schuster
Release Date: 2025-02-25
Get the big picture and the important details with this end-to-end guide for designing highly effective, reliable machine learning systems. From information gathering to release and maintenance, Machine Learning System Design guides you step-by-step through every stage of the machine learning process. Inside, you’ll find a reliable framework for building, maintaining, and improving machine learning systems at any scale or complexity. In Machine Learning System Design: With end-to-end examples you will learn: • The big picture of machine learning system design • Analyzing a problem space to identify the optimal ML solution • Ace ML system design interviews • Selecting appropriate metrics and evaluation criteria • Prioritizing tasks at different stages of ML system design • Solving dataset-related problems with data gathering, error analysis, and feature engineering • Recognizing common pitfalls in ML system development • Designing ML systems to be lean, maintainable, and extensible over time Authors Valeri Babushkin and Arseny Kravchenko have filled this unique handbook with campfire stories and personal tips from their own extensive careers. You’ll learn directly from their experience as you consider every facet of a machine learning system, from requirements gathering and data sourcing to deployment and management of the finished system. About the technology Designing and delivering a machine learning system is an intricate multistep process that requires many skills and roles. Whether you’re an engineer adding machine learning to an existing application or designing a ML system from the ground up, you need to navigate massive datasets and streams, lock down testing and deployment requirements, and master the unique complexities of putting ML models into production. That’s where this book comes in. About the book Machine Learning System Design shows you how to design and deploy a machine learning project from start to finish. You’ll follow a step-by-step framework for designing, implementing, releasing, and maintaining ML systems. As you go, requirement checklists and real-world examples help you prepare to deliver and optimize your own ML systems. You’ll especially love the campfire stories and personal tips, and ML system design interview tips. What's inside • Metrics and evaluation criteria • Solve common dataset problems • Common pitfalls in ML system development • ML system design interview tips About the reader For readers who know the basics of software engineering and machine learning. Examples in Python. About the author Valerii Babushkin is an accomplished data science leader with extensive experience. He currently serves as a Senior Principal at BP. Arseny Kravchenko is a seasoned ML engineer currently working as a Senior Staff Machine Learning Engineer at Instrumental. Table of Contents Part 1 1 Essentials of machine learning system design 2 Is there a problem? 3 Preliminary research 4 Design document Part 2 5 Loss functions and metrics 6 Gathering datasets 7 Validation schemas 8 Baseline solution Part 3 9 Error analysis 10 Training pipelines 11 Features and feature engineering 12 Measuring and reporting results Part 4 13 Integration 14 Monitoring and reliability 15 Serving and inference optimization 16 Ownership and maintenance
Building Machine Learning Systems with Python
Author: Luis Pedro Coelho
language: en
Publisher: Packt Publishing Ltd
Release Date: 2018-07-31
Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine learning system Discover how Python offers multiple algorithms for modern machine learning systems Explore key Python machine learning libraries to implement in your projects Book Description Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems. By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks. What you will learn Build a classification system that can be applied to text, images, and sound Employ Amazon Web Services (AWS) to run analysis on the cloud Solve problems related to regression using scikit-learn and TensorFlow Recommend products to users based on their past purchases Understand different ways to apply deep neural networks on structured data Address recent developments in the field of computer vision and reinforcement learning Who this book is for Building Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python programming is expected.