Resolving Strategy Disagreements in a New Anti-Spam Project
Case context: Your team is starting a new email anti-spam project. The team has several competing ideas: building a custom spelling checker, implementing a sender reputation system, or using complex word-frequency analysis. The team does not have deep domain expertise in spam filtering and is arguing over which direction to design first.
Question: Based on the principles of rapid prototyping, what should the team do first to resolve this disagreement and identify the most promising direction?
Sample answer: The team should not spend time trying to design the perfect system at the outset. Instead, they should build and train a basic anti-spam system as quickly as possible, perhaps in just a few days. Even though this basic system will be far from the best system, they can examine how it functions and perform error analysis on its mistakes. This will quickly provide clues showing which direction is actually the most promising to invest their time in.
Key points:
- Avoid trying to design the perfect system or argue over directions without data
- Build and train a basic anti-spam system quickly (in a few days)
- Use error analysis on the basic system to find clues identifying the most promising direction
Rubric: The answer should recommend building a basic, suboptimal system quickly (in a few days) instead of designing the perfect system first, and specify that error analysis on this basic system will provide the clues needed to choose the most promising direction.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Related
Email Anti-Spam System as a New System Example
Quick Basic-System Advice Targets AI Applications
What is the recommended first step when starting a new ML project in an unfamiliar area?
True or False: You should spend significant time designing the perfect ML system before building or training anything.
After building a basic ML system quickly, you should use _____ to identify the most promising directions for iterative improvement.
Why does Machine Learning Yearning recommend building a basic system quickly rather than designing a perfect system at the outset?
According to Machine Learning Yearning, error analysis should be performed before building any initial system in order to avoid wasted effort.
Machine Learning Yearning recommends building and training a basic system as quickly as possible—perhaps in just a _____ days.
Match each phase of the quick-iteration workflow to its purpose as described in Machine Learning Yearning.
Order the steps of Machine Learning Yearning's recommended workflow for starting a new ML project from beginning through iteration.
What key value does Machine Learning Yearning say you gain by examining a basic system, even when it is far from the best system you could build?
Machine Learning Yearning states that experienced ML practitioners can reliably identify the best project direction before building any system.
After building a basic system, Machine Learning Yearning recommends using _____ to identify the most promising directions and iteratively improve the algorithm.
Match each Machine Learning Yearning recommendation to the reasoning the book provides for it.
Order the reasoning steps that justify the quick-build-and-iterate strategy recommended in Machine Learning Yearning.
Analyzing the Pitfalls of Building a Perfect System at the Outset
Resolving Strategy Disagreements in a New Anti-Spam Project
The Purpose of Examining a Suboptimal Basic System