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Original post with analysis of the book.
Machine Learning System Design
Authors: Arseny Kravchenko, Valerii Babushkin
Publisher: Manning Publications
Length: 376 pages
Practical guide from Babushkin and Kravchenko: problem analysis, metrics, working with data, common mistakes and preparation for ML interviews.
OriginalWhy this book is important
Most ML courses focus on models and algorithms. This book fills the gap - shows full life cycle of an ML system: from problem statement and problem space analysis to release and support.
Valerii Babushkin (Senior Principal at BP) and Arseny Kravchenko(Senior Staff ML Engineer in Instrumental) filled the book with “campfire stories” - real stories from practice that help understand the context of decisions.
Framework for designing ML systems
The book offers a step-by-step framework for creating ML systems of any scale:
1. Problem analysis
- •Defining business goals
- •Problem space analysis
- •Is ML even necessary?
2. Metrics and evaluation
- •Selecting Quality Metrics
- •Success Criteria
- •Baseline and benchmarks
3. Working with data
- •Data collection and markup
- •Error analysis
- •Feature engineering
4. Release and support
- •Deployment strategies
- •Monitoring and alerts
- •Iterative improvements
Key themes of the book
Problem space analysis
Before you write code, you need to understand the problem. The authors teach:
- How to determine if ML is really needed
- How to formulate an ML problem from business requirements
- How to assess the feasibility of a solution before development begins
- How to choose between different approaches (supervised, unsupervised, RL)
Metrics and evaluation criteria
Choosing the right metrics is critical to project success:
- Relationship between business metrics and ML metrics
- Trade-offs between precision/recall, latency/accuracy
- Offline vs online evaluation
- A/B testing of ML systems
Solving data problems
Data is the main source of problems in ML. The book breaks down:
- Data gathering: where and how to collect data
- Data labeling: labeling strategies, crowdsourcing
- Error analysis: systematic search for model errors
- Feature engineering: creating informative features
- Data augmentation and synthetic data
Common mistakes in ML development
The authors have compiled a catalog of common pitfalls:
- Data leakage - when test data “leaks” into training
- Incorrect data split (temporal leakage)
- Overfitting on validation set
- Ignoring edge cases and distribution shift
- Premature optimization of the model instead of data improvement
Prioritization of tasks
One of the unique features of the book is detailed checklists and recommendations for prioritization at different stages of the project:
- Hypothesis Validation
- Simple baseline
- Quick wins
- Error analysis
- Data improvement
- Feature engineering
- Monitoring
- Scaling
- Long-term support
Campfire Stories
A unique feature of the book is “campfire stories”: real stories from the authors’ practice that illustrate theoretical concepts.
These stories show how decisions were made in real projects, what mistakes were made and what lessons were learned. This makes the book practical and memorable.
Related chapter
Interview Approaches
7-step System Design Interview framework.
ML System Design Interview Tips
The book includes a special section on preparing for ML System Design interviews:
How to structure your answer
Typical questions and expectations
Clarifying questions - what to ask
Trade-offs and their rationale
Dealing with Uncertainty
Depth vs breadth of discussion
Key Findings
Start with the problem, not the model. Deep analysis of the problem space is more important than choosing an algorithm.
Simple baseline first. A simple model helps to understand the problem and establishes a starting point.
Data > Model complexity. Improving the data almost always does more than making the model more complex.
Error analysis is your friend. Systematic error analysis shows where to focus your efforts.
Metrics should reflect business goals. Optimizing for the wrong metric is a common cause of project failure.
Plan maintenance from day one. An ML system is not a one-time project, but a living product.
Related chapter
Specifics of ML systems
RADIO for frontend, offline-first for mobile, Feature Store for ML.
Who is this book for?
- ML Engineerswho want to go beyond “train a model” and understand the full development cycle of an ML system
- Data Scientiststransitioning to production ML and wanting to understand the engineering side of the process
- Those preparing for ML System Design interviews in FAANG and other technology companies
- Tech Leads and Managerswho need to understand how to plan and evaluate ML projects