Steps to Turn Process into Machine Learning Model
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Individual Contributor
- Prototype
- Don’t skip—leads to incorrect assumptions later
- Don’t linger—knowledge can become concentrated in 1 person
- Slow to scale
Delegation
- Increase number of employees—100s to millions
- But—tool many stakeholders can result in organizational lock-in—“automation not possible”
- Formalize process
- Org buy in
- Identify humans in ML loop
Digitization
- Computers perform mundane tasks
- Automation
- Lay software and infrastructure foundations—ML extra layer on top
Big Data and Analytics
- Measure everything about ops and users
- Use data to reassess definition of success
- Hand-tuned algorithms—e.g. Google until 2015
Machine Learning
- Complete feedback loop—automate all steps
Moving from Experimentation to Production
- Frame the problem
- Identify the use case
- What?
- Minimum requirements?
- Prepare training data
- Exploratory data analysis—EDA
- Feature engineering—combine features to create new features
- Experimentation phase
- Experiment with different models
- Evaluate
- Package, deploy, monitor