Discover the challenges of black box models during the scoring phase.
- [Instructor] What about scoring black box models?…So called black box models…are just models that have gotten sufficiently complex…that it becomes extremely difficult…to tell a story about your model.…In other words you can't really…interpret it very easily.…Now, black box models are often very accurate.…And they come in multiple forms.…This artificial neural net…is just one of many models…that falls into this category.…
What people like about them…is they can be incredibly accurate.…But they have some disadvantages…that we have to mention here.…Now remember, each line in this diagram…represents a coefficient.…So they're getting quite complex.…That also means that getting your black box model…out of one platform and into another…can be a challenge.…So if you're building the model in once piece of software…but then deploying it somewhere else…you have to deal with that.…One great way to do it…is using something called predictive…model markup language or PMML.…
Now this has been around for many, many years.…And the corporate members of the data mining group…
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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