पाठ्यक्रम
Monitoring Machine Learning Concepts
मध्यमकौशल स्तर
अपडेट किया गया 05/2026TheoryMachine Learning2 घंटे11 वीडियो33 अभ्यास2,050 XP4,714उपलब्धि का प्रमाण पत्र
अपना निःशुल्क खाता बनाएँ
या
जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।हजारों कंपनियों के शिक्षार्थियों द्वारा पसंद किया गया
2 या अधिक लोगों को प्रशिक्षण दे रहे हैं?
DataCamp for Business आज़माएंपाठ्यक्रम विवरण
Machine Learning Monitoring Concepts
Machine learning models influence more and more decisions in the real world. These models need monitoring to prevent failure and ensure that they provide business value to your company. This course will introduce you to the fundamental concepts of creating a robust monitoring system for your models in production.Discover the Ideal Monitoring Workflow
The course starts with the blueprint of where to begin monitoring in production and how to structure the processes around it. We will cover basic workflow by showing you how to detect the issues, identify root causes, and resolve them with real-world examples.Explore the Challenges of Monitoring Models in Production
Deploying a model in production is just the beginning of the model lifecycle. Even if it performs well during development, it can fail due to continuously changing production data. In this course, you will explore the difficulties of monitoring a model’s performance, especially when there’s no ground truth.Understand in Detail Covariate Shift and Concept Drift
The last part of this course will focus on two types of silent model failure. You will understand in detail the different kinds of covariate shifts and concept drift, their influence on the model performance, and how to detect and prevent them.पूर्व आवश्यकताएं
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 Concepts
पाठ्यक्रम पूर्ण
उपलब्धि का प्रमाण पत्र अर्जित करें
इस प्रमाण पत्र को अपनी LinkedIn प्रोफ़ाइल, रिज्यूमे या CV में जोड़ेंइसे सोशल मीडिया पर और अपनी प्रदर्शन समीक्षा में साझा करेंअभी नामांकन करें
19 मिलियन से अधिक शिक्षार्थियों के साथ जुड़ें और आज ही Monitoring Machine Learning Concepts शुरू करें!
अपना निःशुल्क खाता बनाएँ
या
जारी रखने पर, आप हमारी उपयोग की शर्तें, हमारी गोपनीयता नीति को स्वीकार करते हैं और यह भी कि आपका डेटा संयुक्त राज्य अमेरिका में संग्रहीत किया जाता है।मोबाइल के लिए DataCamp के साथ अपने डेटा कौशल को बढ़ाएं
हमारे मोबाइल कोर्स और दैनिक 5 मिनट की कोडिंग चुनौतियों के साथ चलते-फिरते प्रगति करें।