Machine Learning Monitoring Concepts

Starting at USD 39.00
2 hours duration

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Machine Learning Monitoring Concepts As machine learning models become increasingly influential in real-world decision-making, it is crucial to implement monitoring systems to prevent failures and ensure their ongoing business value. This course aims to provide a comprehensive understanding of the fundamental concepts involved in creating a robust monitoring system for models in production. The course begins by outlining the ideal workflow for monitoring in production and establishing effective processes. Through real-world examples, participants will learn how to detect issues, identify their root causes, and implement appropriate resolutions. Deploying a model in production is only the initial step in its lifecycle. Even if a model performs well during development, it can encounter failures due to the ever-changing nature of production data. This course delves into the challenges of monitoring a model's performance, particularly when there is no ground truth available. The final part of the course focuses on two types of silent model failure: covariate shift and concept drift. Participants will gain a detailed understanding of these phenomena, their impact on model performance, and techniques for detecting and preventing them. By the end of this course, participants will possess the knowledge and skills necessary to establish a robust monitoring system for machine learning models in production, ensuring their continued success and value to the organization.

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Online Education Provider ยท 410 courses
DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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