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, you’ll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks.Ben introduces...