Agile machine learning: effective machine learning inspired by the Agile Manifesto
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
New York, NY
Apress
[2019]
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| Series: | For professionals by professionals
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | xvii, 248 Seiten Illustrationen, Diagramme |
| ISBN: | 9781484251065 |
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| adam_text | Table of Contents About the Authors..................................................................................................................xi About the Technical Reviewer................................................................ xiii Introduction.............................................................................. ........................................... xv Chapter 1: Early Delivery............................................................................ Getting Started..............................................................................................................................................3 Data Analysis for Planning......................................................................................................................... 8 Establishing Value...................................................................................................................................... 10 From Early to Continuous Delivery.......................................................................................................... 13 More Entities........................................................................................................................................ 14 More Attributes..................................................................................................................................... 15 More Markets....................................................................................................................................... 17 More
Quality......................................................................................................................................... 18 The Platform as a Product: More Verticals and Customers..........................................................19 Early and Continuous Delivery of Value.................................................................................................. 19 Conclusion.................................................................................................................................................. 24 Chapter 2: Changing Requirements.................................................................................. 25 Building for Change...................................................................................................................................25 Measurement Built for Change..........................................................................................................26 Pipelines Built for Change..................................................................................................................28 Models Built for Change..................................................................................................................... 31 An Architecture to Enable Change.................................................................................................... 42 Tests and Monitoring to Enable Change.................................................................................................44 Monitoring Incremental Change: The Data
DRI...............................................................................44 Sentinel Entities....................................................................................................................................45 Daily Judged Metric............................................................................................................................. 45 V
TABLE OF CONTENTS Testing Features.................................................................................................................................... 46 Testing Learned Models...................................................................................................................... 47 Labeled Training Data...........................................................................................................................48 Responding to Customer DSAT.................................................................................................................50 Identifying Classes of DSATs...............................................................................................................51 Regular Self-Evaluation: Data Wallow and Quality Reviews......................................................... 53 Measuring the Competition.................................................................................................................56 Conclusion.....................................................................................................................................................58 Chapter 3: Continuous Delivery............................................................................... Verifying Code Changes..............................................................................................................................59 The Continuous Integration System......................................................................................................... 61 Continuous Deployment
Systems............................................................................................................ 62 Verifying Data Changes...............................................................................................................................65 Continuous Deployment of Data................................................................................................................67 Deciding What to Ship................................................................................................................................. 68 Conclusion.................................................................................................................................................... 69 Chapter 4: Aligning with the Business............................................................................. 71 The Importance of Daily............................................................................................................................. 72 Advantages of Colocation..........................................................................................................................74 Business-Driven Scrum Teams............... ................................................................................................. 76 Working with the Business to Understand Data.................................................................................... 80 Helping the Business to Understand the Limitations of Machine Learning.................................... 81 Communicating the Rhythm of Engineering to the Business: How We
Do Scrum......................... 83 The Scrum Team.................................................................................................................................... 84 The Portfolio and Product Backlogs................................................................................................. 84 User Stories............................................................................................................................................ 87 Tasks....................................................................................................................................................... 90 The sprint............................................................................................................................................... 96 Communication of Scrum Status to the Business via Email...................................................... 104 Conclusion...................................................................................................................... vi 59
TABLE OF CONTENTS Chapter 5: Motivated Individuals................................ 109 Rewrite Frequently..................................................................................................................................110 Finding and Generating Motivated Individuals...................................................................................111 Interviewing and Recruiting........................................................................................................... 113 Career Management of Motivated Individuals............................................................................. 118 Creating a Productive Environment for Motivated Individuals........................................................ 122 Inner and Outer Loop........................................................................................................................123 Tooling, Monitoring, and Documentation...................................................................................... 124 Developer NSAT................................................................................................................................ 126 Supporting Motivated Individuals Outside Your Organization......................................................... 127 Conclusion............................................................................................................................................... 128 Chapter 6: Effective Communication.............................................................................129 Discussion Around Data Is
Necessarily Interactive........................................................................... 136 Data Tool Basics...................................................................................................................................... 137 Requirements for Data Discussion Tools...................................................................................... 138 Making Quick Evaluations...............................................................................................................139 Mining for Instances.........................................................................................................................141 Sampling Strategies......................................................................................................................... 141 Iterative Differencing........................................................................................................................143 Seeing the Data....................................................................................................................................... 143 Running an Effective Meeting Is a Skill.............................................................................................. 145 Moderated Meetings.........................................................................................................................146 Pair and Parallel Labeling..................................................................................................................... 147 Data
Wallows...........................................................................................................................................148 Demo Meetings....................................................................................................................................... 150 Conclusion............................................................................................................................................... 153 vii
TABLE OF CONTENTS Chapter 7: Monitoring.................................................................................................... 155 Monitoring Working Software................................................................................................................. 155 An Example System: Time to Leave..................................................................................................156 Activity-Based Monitoring..................................................................................................................157 Azure Oata Explorer for Analyzing Traces....................................................................................... 160 What Monitoring Gan Tell You...................................................................................................................162 Is the Working Software Really Working Software?....................................................................162 What Went Wrong?.............................................................................................................................. 163 How Fast Isit?...................................................................................................................................... 163 Are the Business Goals Really Being Met?....................................................................................164 Are the Customer’s Needs Really Being Met?.............................................................................. 166 How Are the Data and Models Being
Used?................................................................................. 166 Conclusion................................................................................................................................................... 168 Chapter 8: Sustainable Development........................................................................... 169 Are We on the Right Sustainable Pace?............................................................................................... 170 Adjusting the Pace Down.................................................................................................................... 171 Adjusting the Pace Up.............................................................................. The Importance of Changes of Pace...................................................................................................... 173 Live Site and Sustainable Pace................................................................................................................175 Sustainable Pace and Multiple Development Geographies............................................................... 177 Conclusion....................................................................................................................................................178 Chapter 9: Technical Excellence..................... 179 Software Engineering Practices for Agility............................................................................................180 Technical Excellence for Data
Projects....................................................................... 1 You Are What You Measure................................................................................................................ 184 Developing Models While Building Metrics................................................................................... 188 Writing Tests for Inference Systems................................................................................................188 Custom Labeling Tools........................................................................................................................ ^1 Storing and Versioning Training and Evaluation Data..................................................................192 Managing Models............................................................................................................................... viii 10Հ
TABLE OF CONTENTS Good Design for Data Projects............................................................................................................. 195 Denotation and Identity in Data Models........................................................................................ 197 Representing Ambiguity..................................................................................................................199 Representing Input.......................................................................................................................... 200 Conclusion.............................................................................................................................................. 201 Chapter 10: Simplicity............... 203 Being Diligent with Task Descriptions................................................................................................. 204 Underspecified Work....................................................................................................................... 204 Deadly Conjunctions........................................................................................................................ 206 Cross-Task Dependencies and Assumptions............................................................................... 206 Early Integration......................................................................................................................................208 Baselines and
Heuristics....................................................................................................................... 208 Recognizing Limits................................................................................................................................. 209 Managing HiPPOs................................................................................................................................... 210 Failing Fast...............................................................................................................................................211 Build or Buy or Open Source.................................................................................................................212 Conclusion...............................................................................................................................................215 Chapter 11 : Self-Organizing Teams............................................................................... 217 Team Compositions................................................................................................................................ 218 Teams Are Made of Individuals............................................................................................................ 219 Individual Traits to Encourage in a Team.............................................................................................221 Managing Across Multiple Self-Organizing Teams............................................................................ 223 Empowered Teams Drive Team
Development and Product Evolution............................................224 How Good Things Emerge..................................................................................................................... 226 Nurturing a Self-Organizing Team........................................................................................................227 Engineering Principles and Conceptual Integrity.............................................................................. 228 Conclusion...............................................................................................................................................229 ix
TABLE OF CONTENTS Chapter 12: Tuning and Adjusting................................................................................... 231 Looking Back.............................................................................................................................................231 The FiveWhys............................................................................................................................................233 Tuning Metrics...........................................................................................................................................234 Looking Forward.......................................................................................................................... 235 Conclusion..................................................................................................................................................236 Chapter 13: Conclusion......................................................................... Index..................................................................................... X
Agile Machine Learning Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors approach models the ground-breaking engineering principles described In the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You ll Learn: • ■ • • • Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and mode! exploration decisions based on the data and the metrics Know the Importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations This book is for anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling,
training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data. Eric Carter has worked as a Partner Group Engineering Manager on the Bing and Cortaría teams at Microsoft. In these roles he worked on search features around products and reviews, business listings, email, and calendar. He currently works on the Microsoft Whiteboard product. Matthew Hurst Is a Principal Engineering Manager and Applied Scientist currently working in the Machine Teaching group at Microsoft. He has worked on a number of teams in Microsoft, including Bing Document Understanding, Local Search, and on various Innovation teams.
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| adam_txt |
Table of Contents About the Authors.xi About the Technical Reviewer. xiii Introduction. . xv Chapter 1: Early Delivery. Getting Started.3 Data Analysis for Planning. 8 Establishing Value. 10 From Early to Continuous Delivery. 13 More Entities. 14 More Attributes. 15 More Markets. 17 More
Quality. 18 The Platform as a Product: More Verticals and Customers.19 Early and Continuous Delivery of Value. 19 Conclusion. 24 Chapter 2: Changing Requirements. 25 Building for Change.25 Measurement Built for Change.26 Pipelines Built for Change.28 Models Built for Change. 31 An Architecture to Enable Change. 42 Tests and Monitoring to Enable Change.44 Monitoring Incremental Change: The Data
DRI.44 Sentinel Entities.45 Daily Judged Metric. 45 V
TABLE OF CONTENTS Testing Features. 46 Testing Learned Models. 47 Labeled Training Data.48 Responding to Customer DSAT.50 Identifying Classes of DSATs.51 Regular Self-Evaluation: Data Wallow and Quality Reviews. 53 Measuring the Competition.56 Conclusion.58 Chapter 3: Continuous Delivery. Verifying Code Changes.59 The Continuous Integration System. 61 Continuous Deployment
Systems. 62 Verifying Data Changes.65 Continuous Deployment of Data.67 Deciding What to Ship. 68 Conclusion. 69 Chapter 4: Aligning with the Business. 71 The Importance of Daily. 72 Advantages of Colocation.74 Business-Driven Scrum Teams. . 76 Working with the Business to Understand Data. 80 Helping the Business to Understand the Limitations of Machine Learning. 81 Communicating the Rhythm of Engineering to the Business: How We
Do Scrum. 83 The Scrum Team. 84 The Portfolio and Product Backlogs. 84 User Stories. 87 Tasks. 90 The sprint. 96 Communication of Scrum Status to the Business via Email. 104 Conclusion. vi 59
TABLE OF CONTENTS Chapter 5: Motivated Individuals. 109 Rewrite Frequently.110 Finding and Generating Motivated Individuals.111 Interviewing and Recruiting. 113 Career Management of Motivated Individuals. 118 Creating a Productive Environment for Motivated Individuals. 122 Inner and Outer Loop.123 Tooling, Monitoring, and Documentation. 124 Developer NSAT. 126 Supporting Motivated Individuals Outside Your Organization. 127 Conclusion. 128 Chapter 6: Effective Communication.129 Discussion Around Data Is
Necessarily Interactive. 136 Data Tool Basics. 137 Requirements for Data Discussion Tools. 138 Making Quick Evaluations.139 Mining for Instances.141 Sampling Strategies. 141 Iterative Differencing.143 Seeing the Data. 143 Running an Effective Meeting Is a Skill. 145 Moderated Meetings.146 Pair and Parallel Labeling. 147 Data
Wallows.148 Demo Meetings. 150 Conclusion. 153 vii
TABLE OF CONTENTS Chapter 7: Monitoring. 155 Monitoring Working Software. 155 An Example System: Time to Leave.156 Activity-Based Monitoring.157 Azure Oata Explorer for Analyzing Traces. 160 What Monitoring Gan Tell You.162 Is the Working Software Really Working Software?.162 What Went Wrong?. 163 How Fast Isit?. 163 Are the Business Goals Really Being Met?.164 Are the Customer’s Needs Really Being Met?. 166 How Are the Data and Models Being
Used?. 166 Conclusion. 168 Chapter 8: Sustainable Development. 169 Are We on the Right Sustainable Pace?. 170 Adjusting the Pace Down. 171 Adjusting the Pace Up. The Importance of Changes of Pace. 173 Live Site and Sustainable Pace.175 Sustainable Pace and Multiple Development Geographies. 177 Conclusion.178 Chapter 9: Technical Excellence. 179 Software Engineering Practices for Agility.180 Technical Excellence for Data
Projects. 1 You Are What You Measure. 184 Developing Models While Building Metrics. 188 Writing Tests for Inference Systems.188 Custom Labeling Tools. ^1 Storing and Versioning Training and Evaluation Data.192 Managing Models. viii 10Հ
TABLE OF CONTENTS Good Design for Data Projects. 195 Denotation and Identity in Data Models. 197 Representing Ambiguity.199 Representing Input. 200 Conclusion. 201 Chapter 10: Simplicity. 203 Being Diligent with Task Descriptions. 204 Underspecified Work. 204 Deadly Conjunctions. 206 Cross-Task Dependencies and Assumptions. 206 Early Integration.208 Baselines and
Heuristics. 208 Recognizing Limits. 209 Managing HiPPOs. 210 Failing Fast.211 Build or Buy or Open Source.212 Conclusion.215 Chapter 11 : Self-Organizing Teams. 217 Team Compositions. 218 Teams Are Made of Individuals. 219 Individual Traits to Encourage in a Team.221 Managing Across Multiple Self-Organizing Teams. 223 Empowered Teams Drive Team
Development and Product Evolution.224 How Good Things Emerge. 226 Nurturing a Self-Organizing Team.227 Engineering Principles and Conceptual Integrity. 228 Conclusion.229 ix
TABLE OF CONTENTS Chapter 12: Tuning and Adjusting. 231 Looking Back.231 The FiveWhys.233 Tuning Metrics.234 Looking Forward. 235 Conclusion.236 Chapter 13: Conclusion. Index. X
Agile Machine Learning Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors' approach models the ground-breaking engineering principles described In the Agile Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll Learn: • ■ • • • Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and mode! exploration decisions based on the data and the metrics Know the Importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations This book is for anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling,
training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data. Eric Carter has worked as a Partner Group Engineering Manager on the Bing and Cortaría teams at Microsoft. In these roles he worked on search features around products and reviews, business listings, email, and calendar. He currently works on the Microsoft Whiteboard product. Matthew Hurst Is a Principal Engineering Manager and Applied Scientist currently working in the Machine Teaching group at Microsoft. He has worked on a number of teams in Microsoft, including Bing Document Understanding, Local Search, and on various Innovation teams. |
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| discipline_str_mv | Informatik |
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| title_full | Agile machine learning effective machine learning inspired by the Agile Manifesto Eric Carter, Matthew Hurst |
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| title_short | Agile machine learning |
| title_sort | agile machine learning effective machine learning inspired by the agile manifesto |
| title_sub | effective machine learning inspired by the Agile Manifesto |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Software Engineering (DE-588)4116521-4 gnd Big Data (DE-588)4802620-7 gnd |
| topic_facet | Maschinelles Lernen Software Engineering Big Data |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032160434&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032160434&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT cartereric agilemachinelearningeffectivemachinelearninginspiredbytheagilemanifesto AT hurstmatthew agilemachinelearningeffectivemachinelearninginspiredbytheagilemanifesto |