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:
您也可以从以下列表中选择网站:
如何获得最佳网站性能
选择中国网站(中文或英文)以获得最佳网站性能。其他 MathWorks 国家/地区网站并未针对您所在位置的访问进行优化。
美洲
- América Latina (Español)
- Canada (English)
- United States (English)
欧洲
- 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)
亚太
- Australia (English)
- India (English)
- New Zealand (English)
- 中国
- 日本Japanese (日本語)
- 한국Korean (한국어)