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Machine Learning System Design Document Template
Executive Summary
- Project Name: [Name]
- Problem Statement: [Brief description of the problem]
- Business Impact: [Expected impact and KPIs]
- Timeline: [High-level timeline]
I. Problem Definition
i. Origin
- What is the core problem?
- Who are the stakeholders?
- What are the current limitations/challenges?
- What is the current workflow?
ii. Relevance & Reasons
- Why is this problem important?
- What are the expected benefits?
- What is the estimated business impact?
- How much does the current problem cost?
iii. Previous Work
- What existing solutions have been tried?
- What worked/didn’t work?
- What can we learn from past attempts?
- Can we improve existing solutions?
iv. Other Issues & Risks
- What infrastructure requirements exist?
- What are the potential failure modes?
- What is the cost of mistakes?
- What checks and balances are needed?
II. Metrics and Losses
i. Metrics
- What are the key business metrics?
- What are the model performance metrics?
- How do these align with business goals?
- What are the trade-offs between metrics?
ii. Loss Functions
- What loss functions will be used?
- How do they relate to business metrics?
- What are the trade-offs?
- How will we handle edge cases?
III. Dataset
i. Data Sources
- What internal data sources are available?
- What external data can be used?
- What are the data quality issues?
- How fresh is the data?
ii. Data Labeling
- How will data be labeled?
- What is the labeling process?
- How will we ensure quality?
- What are the costs?
- What metadata is available?
- How will it be used?
- What additional context can it provide?
iv. Data Quality Issues
- What quality issues exist?
- How will they be addressed?
- What is the data cleaning process?
- How will we handle missing data?
v. ETL Pipeline
- How will data be collected?
- What transformations are needed?
- How will we handle updates?
- What is the refresh frequency?
IV. Validation Schema
i. Requirements
- What are the validation requirements?
- How will we prevent data leakage?
- What temporal constraints exist?
ii. Inference Process
- How will the model make predictions?
- What is the prediction horizon?
- What constraints must be considered?
iii. Inner and Outer Loops
- How will we validate the model?
- What cross-validation strategy will be used?
- How will we handle time series data?
iv. Update Frequency
- How often will the model be updated?
- What triggers an update?
- How will we handle data drift?
V. Baseline Solution
i. Constant Baseline
- What simple baselines will we use?
- How will we measure against them?
- What are the minimum acceptable results?
ii. Model Baselines
- What model architectures will we try?
- What are the trade-offs?
- How will we compare them?
iii. Feature Baselines
- What features will we start with?
- How will we measure feature importance?
- What feature engineering is needed?
VI. Error Analysis
i. Learning Curve Analysis
- How will we analyze learning curves?
- What patterns are we looking for?
- How will we handle overfitting/underfitting?
ii. Residual Analysis
- How will we analyze residuals?
- What distributions do we expect?
- How will we handle outliers?
iii. Best/Worst Case Analysis
- How will we identify edge cases?
- What are the failure modes?
- How will we improve worst cases?
VII. Training Pipeline
i. Overview
- What is the training architecture?
- What tools will we use?
- How will we ensure reproducibility?
ii. Data Preprocessing
- What preprocessing is needed?
- How will we handle feature engineering?
- What normalization is required?
iii. Model Training
- What is the training process?
- How will we handle hyperparameters?
- What hardware requirements exist?
iv. Experiment Tracking
- How will we track experiments?
- What metrics will we log?
- How will we version models?
VIII. Features
i. Feature Selection Criteria
- What criteria will we use?
- How will we measure importance?
- What are the computational constraints?
ii. Feature List
- What features will we use?
- What transformations are needed?
- What are the dependencies?
iii. Feature Tests
- How will we test features?
- What quality checks are needed?
- How will we handle drift?
IX. Measuring and Reporting
i. Measuring Results
- How will we measure success?
- What metrics will we track?
- How will we report results?
ii. A/B Testing
- What is the testing strategy?
- How will we split traffic?
- What are the success criteria?
iii. Reporting Results
- What reports will be generated?
- Who are the stakeholders?
- How will results be communicated?
X. Integration
i. Fallback Strategies
- What are the fallback plans?
- When do we fall back?
- How do we recover?
ii. API Design
- What APIs will we expose?
- What are the interfaces?
- What are the SLAs?
iii. Release Cycle
- How will we release updates?
- What is the deployment strategy?
- How will we handle rollbacks?
iv. Operational Concerns
- How will we monitor the system?
- What alerts are needed?
- How will we handle incidents?