# Monitoring Machine Learning 개념
This is a DataCamp course: 프로덕션 환경의 머신러닝 모델 모니터링 과제(데이터·컨셉 드리프트)와 모델 성능 저하를 해결하는 방법을 학습합니다.
## Course Details
- **Duration:** ~2h
- **Level:** Intermediate
- **Instructor:** Hakim Elakhrass
- **Students:** ~19,440,000 learners
- **Subjects:** Theory, Machine Learning, Python, Emerging Technologies
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** MLOps Concepts, Supervised Learning with scikit-learn
## Learning Outcomes
- Theory
- Machine Learning
- Python
- Emerging Technologies
- Monitoring Machine Learning 개념
## Traditional Course Outline
1. What is ML Monitoring - The first chapter will explain why businesses need to monitor your machine learning models in production. You will learn about the ideal monitoring workflow and the steps involved, as well as some of the challenges that monitoring systems can face in production.
2. Theoretical Concepts of monitoring - In Chapter 2, you'll discover the fundamental importance of performance monitoring in a reliable monitoring system. We'll explore the common challenges faced in real-world production environments, such as the availability of ground truth. By the end of the chapter, you'll know how to handle situations when ground truth data is delayed or absent , using performance estimation algorithms.
3. Covariate Shift and Concept Drift Detection - Now that you know the basics of covariate shift and concept drift in production, let''s dive a little bit deeper. At the end of this chapter, you will know the different ways to detect and handle them in real-world scenarios.
## Resources and Related Learning
**Related tracks:** 머신 러닝 엔지니어, 실제 운영 환경에서의 머신러닝 파이썬에서
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Monitoring Machine Learning 개념
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MLOps ConceptsSupervised Learning with scikit-learn1
What is ML Monitoring
The first chapter will explain why businesses need to monitor your machine learning models in production. You will learn about the ideal monitoring workflow and the steps involved, as well as some of the challenges that monitoring systems can face in production.
2
Theoretical Concepts of monitoring
In Chapter 2, you'll discover the fundamental importance of performance monitoring in a reliable monitoring system. We'll explore the common challenges faced in real-world production environments, such as the availability of ground truth. By the end of the chapter, you'll know how to handle situations when ground truth data is delayed or absent , using performance estimation algorithms.
3
Covariate Shift and Concept Drift Detection
Now that you know the basics of covariate shift and concept drift in production, let''s dive a little bit deeper. At the end of this chapter, you will know the different ways to detect and handle them in real-world scenarios.
Monitoring Machine Learning 개념
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