Learn why the data prep that is developed during the modeling phase needs to be modified to maximize performance at deployment.
- [Instructor] Now let's talk about data prep and scoring.…Let's revisit the monthly statements.…I want to discuss in some detail…how this worked on an actual project.…At this particular client's site,…there were eight different billing statements…so customers would get their monthly billing…every couple of days,…some folks got it on the 4th, some on the 8th and so on.…So on the evenings that those monthly statements…were about to go out,…all of those new billing variables were calculated.…In an hour later, at one in the morning,…all the data preparation was done.…
So what those calculations were…were all the feature engineering…that the modelers had prototyped during the modeling phase…and that they ultimately used in the model.…And then, one hour after that,…at two in the morning, the scores were calculated.…So what folks often forget to take in account…that scoring is not just about the model…but also about all that data preparation…that generates the variables.…After all, it's usually the feature engineered variables…
Note: This course is software agnostic. The emphasis is on strategy and planning. Examples, calculations, and software results shown are for training purposes only.
- Evaluating the proper amount of data
- Assessing data quality and quantity
- Seasonality and time alignment
- Data preparation challenges
- Data modeling challenges
- Scoring machine-learning models
- Deploying models and adjusting data prep and scoring
- Monitoring and maintenance
Skill Level Beginner
Machine Learning and AI Foundations: Recommendationswith Adam Geitgey58m 7s Intermediate
Deploying Scalable Machine Learning for Data Sciencewith Dan Sullivan1h 43m Intermediate
Defining terms1m 48s
1. The Phases of a Machine Learning Project
2. Designing a Machine Learning Dataset
3. Data Prep Challenges
4. Modeling Challenges
7. Monitoring and Maintenance
Next steps1m 1s
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