Review the definition of monitoring from CRISP-DM.
- [Instructor] The cross-industry standard process…for data mining describes monitoring…and maintenance in the following way.…They're important issues if the data mining results becomes…part of the day-to-day business and its environment.…A careful preparation of a maintenance strategy helps…to avoid unnecessarily long periods of incorrect usage…of data mining results.…I want to elaborate on this in a little bit more detail…and then provide a case study.…Some other considerations that CRISP-DM provides.…When should the data mining result…or model not be used anymore?…Identify criteria, here's the important stuff, folks.…
There's some threshold about which…or below which we start to worry about the model,…and you want this determined in advance.…Things like accuracy, also changes to the environment.…It could simply be that the business goal…that we had is no longer there.…What should happen specifically if the model…or result can no longer be used?…Will the business objectives of the use…of the model change over time?…
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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