Coursera - Machine Learning in Production - Week 1 - Section 1- The Machine Learning Project Lifecycle
2025年01月02日
Keywords: data drift (or concept drift)
References
Hidden Technical Debt in Machine Learning Systems
References
Steps of an ML Project
VAD: Voice Activity Detection
References
Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics
Detecting data drift using Amazon SageMaker
2. Modeling
3. Data
Optional: Scoping
MLOps (Machine Learning Operations) is an emerging discipline, and comprises a set of tools and principles to support progress through the ML project lifecycle.
DeepLearning.AI Forum
Week 1: Overview of the ML Lifecycle and Deployment
Section 1: The Machine Learning Project Lifecycle
1. Welcome
Keywords: data drift (or concept drift)
References
Hidden Technical Debt in Machine Learning Systems
2. Steps of an ML Project
References
Steps of an ML Project
3. Case study: speech recognition
VAD: Voice Activity Detection
References
Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics
Detecting data drift using Amazon SageMaker
4. Course outline
1. Deployment2. Modeling
3. Data
Optional: Scoping
MLOps (Machine Learning Operations) is an emerging discipline, and comprises a set of tools and principles to support progress through the ML project lifecycle.
5. Intake Survey
6. [IMPORTANT] Have questions, issues or ideas? Join our Forum!
DeepLearning.AI Forum