Discover a trick to effectively combine batch and real-time scoring.
- [Instructor] Now let's revisit the call center example…and see if there are circumstances under which…we would definitely want real-time scoring.…For instance, let's say someone's calling in…around a cell phone repair, or possibly even an upgrade,…and they've had three complaints over the last seven days.…It would certainly seem inappropriate…to not take that recent activity into account if we can,…so what would be the most efficient way to do so?…This is what people forget.…
We can actually do a mix of batch and real-time.…We can do our standard model,…which is taking our billing data only updated monthly,…we can run it through our model,…and we generate our churn score.…Keep in mind that this model is using dozens of variables.…The data prep takes several minutes to run.…It's run in batch.…The system's already there.…We want to leverage this, not rebuild it,…so this is what we do.…We take that churn score from the first model,…and we simply add to it only those things…that change between billing statements.…
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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