Machine Learning Engineering in Action
Ben Wilson has almost ten years of experience as a professional data scientist. He is currently a resident solutions architect at Databricks, where he works with enterprises ranging from 5-person startups to worldwide Fortune 100. Ben founded and leads the Databricks Labs AutoML project, a Scala and Python-based toolkit that simplifies machine learning feature engineering, model tuning, and pipeline-enabled modeling.
Machine Learning Engineering in Action teaches you:
- Evaluating data science problems in order to discover the best answer
- Scoping a machine learning project for budget and usage assumptions
- Process approaches that reduce waste and speed up production
- Using standardized prototyping work and statistical validation to evaluate a project
- Selecting the appropriate technology and tools for your project
- Making your codebase easier to comprehend, maintain, and test
- Streamlining your troubleshooting and logging procedures
It is not easy to get a machine learning project from your data science team to your end users. Machine Learning Engineering in Action will assist you in simplifying the process. Inside, you'll find terrific guidance from Databricks' Principal Resident Solutions Architect, Ben Wilson.
Ben describes his personal toolbox for developing deployable and maintainable production machine learning systems. You'll discover the value of Agile approaches for rapid prototyping and collaboration with stakeholders, as well as a fresh understanding for the value of planning. Adopting well-established software development standards will help you achieve better code management and make testing, scaling, and reusing machine learning code easier. Every method is described in an approachable, peer-to-peer format and illustrated with production-ready source code.
Author: Ben Wilson
Link to buy: https://www.amazon.com/Machine-Learning-Engineering-Action-Wilson/dp/1617298719/
Ratings: 5.0 out of 5 stars (from 2 reviews)
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