Machine Learning Engineering in Action

Field-Tested Tips, Tricks, And Design Patterns For Building Machine Learning Projects That Are Deployable, Maintainable, And Secure From Concept To Production.In Machine Learning Engineering In Action, You Will Learn:Evaluating Data Science Problems To Find The Most Effective Solutionscoping A Machine Learning Project For Usage Expectations And Budgetprocess Techniques That Minimize Wasted Effort And Speed Up Productionassessing A Project Using Standardized Prototyping Work And Statistical Validationchoosing The Right Technologies And Tools For Your Projectmaking Your Codebase More Understandable, Maintainable, And Testableautomating Your Troubleshooting And Logging Practicesferrying A Machine Learning Project From Your Data Science Team To Your End Users Is No Easy Task. Machine Learning Engineering In Action Will Help You Make It Simple. Inside, YouLl Find Fantastic Advice From Veteran Industry Expert Ben Wilson, Principal Resident Solutions Architect At Databricks.Ben Introduces His Personal Toolbox Of Techniques For Building Deployable And Maintainable Production Machine Learning Systems. YouLl Learn The Importance Of Agile Methodologies For Fast Prototyping And Conferring With Stakeholders, While Developing A New Appreciation For The Importance Of Planning. Adopting Well-Established Software Development Standards Will Help You Deliver Better Code Management, And Make It Easier To Test, Scale, And Even Reuse Your Machine Learning Code. Every Method Is Explained In A Friendly, Peer-To-Peer Style And Illustrated With Production-Ready Source Code.About The Technologydeliver Maximum Performance From Your Models And Data. This Collection Of Reproducible Techniques Will Help You Build Stable Data Pipelines, Efficient Application Workflows, And Maintainable Models Every Time. Based On Decades Of Good Software Engineering Practice, Machine Learning Engineering Ensures Your Ml Systems Are Resilient, Adaptable, And Perform In Production.Purchase Of The Print Book Includes A Free Ebook In Pdf, Kindle, And Epub Formats From Manning Publications.About The Bookmachine Learning Engineering In Action Teaches You Core Principles And Practices For Designing, Building, And Delivering Successful Machine Learning Projects. YouLl Discover Software Engineering Techniques Like Conducting Experiments On Your Prototypes And Implementing Modular Design That Result In Resilient Architectures And Consistent Cross-Team Communication. Based On The AuthorS Extensive Experience, Every Method In This Book Has Been Used To Solve Real-World Projects.WhatS Insidescoping A Machine Learning Project For Usage Expectations And Budgetchoosing The Right Technologies For Your Designmaking Your Codebase More Understandable, Maintainable, And Testableautomating Your Troubleshooting And Logging Practicesabout The Readerfor Data Scientists Who Know Machine Learning And The Basics Of Object-Oriented Programming.About The Authorben Wilson Is Principal Resident Solutions Architect At Databricks, Where He Developed The Databricks Labs Automl Project, And Is An Mlflow Committer.Table Of Contentspart 1 An Introduction To Machine Learning Engineering1 What Is A Machine Learning Engineer?2 Your Data Science Could Use Some Engineering3 Before You Model: Planning And Scoping A Project4 Before You Model: Communication And Logistics Of Projects5 Experimentation In Action: Planning And Researching An Ml Project6 Experimentation In Action: Testing And Evaluating A Project7 Experimentation In Action: Moving From Prototype To Mvp8 Experimentation In Action: Finalizing An Mvp With Mlflow And Runtime Optimizationpart 2 Preparing For Production: Creating Maintainable Ml9 Modularity For Ml: Writing Testable And Legible Code10 Standards Of Coding And Creating Maintainable Ml Code11 Model Measurement And Why ItS So Important12 Holding On To Your Gains By Watching For Drift13 Ml Development Hubrispart 3 Developing Production Machine Learning Code14 Writing Production Code15 Quality And Acceptance Testing16 Production Infrastructure