Managing Machine Learning Projects From design to deployment - MEAP Version 4

Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required!In Managing Machine Learning Projects you'll learn essential machine learning project management techniques, including: Understanding an ML project's requirements Setting up the infrastructure for the project and resourcing a team Working with clients and other stakeholders Dealing with data resources and bringing them into the project for use Handling the lifecycle of models in the project Managing the application of ML algorithms Evaluating the performance of algorithms and models Making decisions about which models to adopt for delivery Taking models through development and testing Integrating models with production systems to create effective applications Steps and behaviors for managing the ethical implications of ML technology Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You'll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book's strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues. About the Technology Ferrying machine learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices you'll need to ensure your projects succeed. About the Book Managing Machine Learning Projects is an amazing source of battle-tested techniques for effective delivery of real-life machine learning solutions. The book is laid out across a series of sprints that take you from a project proposal all the way to deployment into production. You'll learn how to plan essential infrastructure, coordinate experimentation, protect sensitive data, and reliably measure model performance. Many ML projects fail to create real value—read this book to make sure your project is a success. What's Inside Set up infrastructure and resource a team Bring data resources into a project Accurately estimate time and effort Evaluate which models to adopt for delivery Integrate models into effective applications About the Reader For anyone interested in better management of machine learning projects. No technical skills required. About the Author Simon Thompson has spent 25 years developing AI systems to create applications for use in telecoms, customer service, manufacturing and capital markets. He led the AI research program at BT Labs in the UK, and is now the Head of Data Science at GFT Technologies. Table of Contents 1 Introduction: Delivering machine learning projects is hard; let's do it better 2 Pre-project: From opportunity to requirements 3 Pre-project: From requirements to proposal 4 Getting started 5 Diving into the problem 6 EDA, ethics, and baseline evaluations 7 Making useful models with ML 8 Testing and selection 9 Sprint 3: system building and production 10 Post project (sprint O) Managing Machine Learning Projects MEAP V04 Copyright welcome brief contents Chapter 1: Introduction: Delivering Machine Learning projects is hard, let’s do it better 1.1 What is Machine Learning? 1.2 Why is ML Important? 1.3 Waterfall, Agile, Devops 1.4 Specialist Approaches for ML System Development 1.5 Understanding this Book. 1.6 Case study: The bike shop 1.7 Summary 1.8 References Chapter 2: Pre-project: from opportunity to requirements 2.1 Pre-Project Backlog 2.2 Project Management Infrastructure 2.3 Understanding Requirements 2.3.1 Funding Model 2.3.2 Business Requirements Business Requirements: Why? Business Requirements: Who? Business Requirements: What? 2.4 Data 2.5 Security & Privacy 2.6 Corporate Responsibility, Regulation & Ethical considerations 2.7 Development Architecture and Process 2.7.1 Development Environment 2.7.2 Production Architecture 2.8 Summary & Takeaways 2.9 References Chapter 3: Pre-project: from requirements to a proposal 3.1 Project Hypothesis 3.2 Create an Estimate 3.2.1 Time and Effort estimates 3.2.2 Team Design for ML Projects 3.2.3 Project Risks 3.3 Presales/Pre-Project Administration 3.4 Pre-project/presales checklist 3.5 The Bike Shop Presales 3.6 Pre-Project Post-Script 3.7 Summary 3.8 References Chapter 4: Sprint Zero: Getting started 4.1 Sprint Zero Backlog 4.2 Finalize Team Design & Resourcing 4.3 A Way of Working 4.3.1 Process & Structure 4.3.2 Heartbeat and Communication Plan 4.3.3 Tooling Data Pipelining Versioning Data Testing 4.3.4 Standards & Practices 4.3.5 Documentation 4.4 Infrastructure Plan 4.4.1 System Access 4.4.2 Technical Infrastructure Evaluation 4.5 The Data Story 4.5.1 Data Collection Motivation 4.5.2 Collection Mechanism 4.5.3 Lineage 4.5.4 Events 4.6 Privacy and Security & Ethics Plan 4.7 Project Roadmap 4.8 Sprint 0 Checklist 4.9 Bike Shop: Project Set-up 4.10 Summary 4.11 References Chapter 5: Sprint 1: Diving into the problem 5.1 Sprint-1 Backlog 5.2 Understanding the Data 5.2.1 The Data Survey 5.2.2 Surveying numerical data 5.2.3 Surveying categorical data 5.2.4 Surveying unstructured data 5.2.5 Reporting and using the survey 5.3 Business Problem Refinement, UX and Application Design 5.4 Building Data Pipelines 5.4.1 Data Fusion Challenges 5.4.2 Pipeline Jungles 5.4.3 Data Testing 5.5 Model Repository and Model Versioning 5.5.1 Features, Foundational Models, and Training Regimes 5.5.2 Overview of Versioning 5.6 Summary 5.7 References Chapter 6: Sprint 1: EDA, ethics, baseline evaluation 6.1 Exploratory Data Analysis (EDA) 6.1.1 EDA Objectives 6.1.2 Summarizing and Describing Data. 6.1.3 Plots and visualisations 6.1.4 Unstructured Data 6.2 Ethics Checkpoint 6.3 Baseline Models and Performance 6.4 What if there are Problems? 6.5 Pre-modelling checklist 6.6 The Bike Shop: Pre-modelling 6.7 Summary 6.8 Works Cited Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required! In Managing Machine Learning Projects youll learn essential machine learning project management techniques, Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. Youll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The books strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Ferrying machine learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices youll need to ensure your projects succeed. About the Book Managing Machine Learning Projects is an amazing source of battle-tested techniques for effective delivery of real-life machine learning solutions. The book is laid out across a series of sprints that take you from a project proposal all the way to deployment into production. Youll learn how to plan essential infrastructure, coordinate experimentation, protect sensitive data, and reliably measure model performance. Many ML projects fail to create real valueread this book to make sure your project is a success. What's Inside About the Reader For anyone interested in better management of machine learning projects. No technical skills required. About the Author Simon Thompson has spent 25 years developing AI systems to create applications for use in telecoms, customer service, manufacturing and capital markets. He led the AI research program at BT Labs in the UK, and is now the Head of Data Science at GFT Technologies. Table of Contents 1 Delivering machine learning projects is hard; lets do it better 2 From opportunity to requirements 3 From requirements to proposal 4 Getting started 5 Diving into the problem 6 EDA, ethics, and baseline evaluations 7 Making useful models with ML 8 Testing and selection 9 Sprint 3: system building and production 10 Post project (sprint O)