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This lesson summarizes the guidelines learned from these
real-world examples.
Real World Guidelines
Some Effective ML Guidelines
- Keep the first model simple
Some Effective ML Guidelines
- Keep the first model simple
- Focus on ensuring data pipeline correctness
Some Effective ML Guidelines
- Keep the first model simple
- Focus on ensuring data pipeline correctness
- Use a simple, observable metric for training & evaluation
Some Effective ML Guidelines
- Keep the first model simple
- Focus on ensuring data pipeline correctness
- Use a simple, observable metric for training & evaluation
- Own and monitor your input features
Some Effective ML Guidelines
- Keep the first model simple
- Focus on ensuring data pipeline correctness
- Use a simple, observable metric for training & evaluation
- Own and monitor your input features
- Treat your model configuration as code: review it, check it in
Some Effective ML Guidelines
- Keep the first model simple
- Focus on ensuring data pipeline correctness
- Use a simple, observable metric for training & evaluation
- Own and monitor your input features
- Treat your model configuration as code: review it, check it in
- Write down the results of all experiments, especially "failures"
Video Lecture Summary
Here's a quick synopsis of effective ML guidelines:
- Keep your first model simple.
- Focus on ensuring data pipeline correctness.
- Use a simple, observable metric for training & evaluation.
- Own and monitor your input features.
- Treat your model configuration as code: review it, check it in.
- Write down the results of all experiments, especially "failures."
Other Resources
Rules of Machine Learning contains additional guidance.