Explore a comparison of four different companies and their data volumes and discover some implications for modeling and infrastructure.
- [Instructor] We've seen that technical requirements…like partitioning and balancing…have a dramatic impact on the amount of data…that actually gets to those modeling algorithms.…So who truly has big data?…Well remarkably LinkedIn has…a half billion subscribers worldwide.…But even more dramatic to me is…a quarter million log ins monthly,…and nine billion content impressions weekly.…Netflix, 120 million subscribers worldwide.…
37% of the U.S. is a Netflix subscriber,…and another remarkable number,…eight million events per second.…So clearly no one's going to question that we're talking…about big data here.…Let's talk about two somewhat disguise client examples…that I have for you.…What I've done is kind of combined…a couple examples into one.…So a regional healthcare provider…was looking at 30 day re-admit.…So someone leaves the hospital,…how many of those individuals will have…to be readmitted within 30 days.…
They were looking at a dramatic two billion transactions.…Transaction here could be something like…a blood draw for instance.…
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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