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scenarios for ml-models
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Classroom Contents
Balancing Speed and Accuracy in Model Development
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- 1 intro
- 2 preamble
- 3 data scientist at about & render, london, uk
- 4 today's talk
- 5 the essence of balance: speed vs accuracy
- 6 factors impacting accuracy and speed
- 7 the business impact of speed and accuracy
- 8 real-world examples
- 9 balancing act: speed, accuracy, and cost
- 10 strategic importance of the balance
- 11 how to understand business objectives
- 12 scenarios for ml-models
- 13 optimisation strategies
- 14 training data quality and quantity
- 15 what is a good dataset?
- 16 what is a bad dataset?
- 17 data pre-processing
- 18 how to find inefficiencies in data pre-processing?
- 19 yappi
- 20 most common inefficiencies
- 21 feature selection
- 22 shap values for feature selection
- 23 model selection
- 24 xgboost
- 25 lightgbm
- 26 how to choose the best option
- 27 a quick recap
- 28 thank you for your time!
