Machine Learning System Design is a practical guide to designing efficient and reliable machine learning systems. The book covers the entire ML system development cycle: from data collection to release and maintenance, offering a clear step-by-step structure that will aid both beginners and experienced professionals.
Machine Learning System Design
0h 0m 0s
English
Paid
You will learn how to:
- See the big picture in ML system design
- Analyze tasks and choose optimal ML solutions
- Pass interviews on ML system design
- Select metrics and evaluation criteria
- Properly prioritize at different stages of the project
- Work with data issues: collection, error analysis, feature generation
- Avoid common mistakes when developing ML systems
- Design systems that are easy to maintain and develop
About the Technologies
Developing a machine learning system is a complex process requiring knowledge in engineering, data analysis, and model operation. Whether you are creating an ML system from scratch or integrating a model into an existing application, you will work with large volumes of data, set up testing, monitoring, and deployment processes. This book will be your guide in this process.
Features
The book includes practical checklists, examples from real projects, tips for interviews, as well as "campfire tales" - practical stories and observations from the author accumulated over years of experience.
About the Authors
Arseny Kravchenko
Arseny Kravchenko is a software engineer and ML practitioner publishing course material on the system-design side of machine-learning work — the architectural decisions that show up when ML systems move from notebooks to production.
His CourseFlix listing carries Machine Learning System Design — a focused treatment of the ML-system-design discipline: feature stores, model serving, monitoring, retraining loops, A/B testing for models, and the trade-offs that separate working production ML from research-grade ML.
Material is paid and aimed at ML engineers preparing for ML-system-design interviews or doing real architectural work on production ML systems. For broader content, see CourseFlix's Machine learning category page.
Valerii Babushkin
Valerii Babushkin is a Russian-British data scientist (formerly at Yandex, X5 Retail Group, and Bolt) and the co-author of Machine Learning System Design — one of the more rigorous practitioner-focused books on the engineering discipline of ML systems at scale.
His CourseFlix listing carries Machine Learning System Design. Material is paid and aimed at ML engineers preparing for senior-level system-design interviews focused on ML / data systems specifically.
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Frequently asked questions
What is Machine Learning System Design about?
Machine Learning System Design is a practical guide to designing efficient and reliable machine learning systems. The book covers the entire ML system development cycle: from data collection to release and maintenance, offering a clear…
Who teaches this course?
It is taught by Arseny Kravchenko, Valerii Babushkin. You can find more courses by these instructors on the corresponding source pages.
How long is the course?
It is delivered as a self-paced online course on CourseFlix.
Is it free to watch?
It is part of CourseFlix's premium catalog. A subscription unlocks the full video player; the course description, table of contents, and preview information are available to everyone.
Where can I watch it online?
The course is available to watch online on CourseFlix at https://courseflix.net/course/machine-learning-system-design. The page hosts every lesson with the integrated video player; no download is required.