Change for the Better: Improving Predictions by Automating Drift Detection
Drifting data poses three problems: detecting and assessing drift-related model performance degradation; generating a more accurate model from the new data; and deploying a new model into an existing machine-learning pipeline. Using a real-world predictive maintenance problem as an example, we demonstrate a solution that addresses each of these challenges. We reduce the complexity and costs of operating the system - as well as increase its reliability - by automating both drift detection and data labelling. After watching this video, you will understand how to develop streaming analytics on a desktop, deploy those solutions to the cloud, and apply AutoML strategies to keep your models up-to-date and their predictions as accurate as possible.
Recorded during Big Things Conference 2021
Published: 7 Dec 2021
Featured Product
Statistics and Machine Learning Toolbox
Up Next:
Related Videos:
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)
Asia Pacific
- Australia (English)
- India (English)
- New Zealand (English)
- 中国
- 日本Japanese (日本語)
- 한국Korean (한국어)