In the fast-paced world of data science, keeping your machine learning models up-to-date and relevant is a never-ending job. The data never stays the same for long! In this course, Continuous Model Training with Evolving Data Streams, you’ll gain the ability to maintain accurate models, no matter how much the data changes. First, you’ll explore why continuous training is so important, delving into topics like concept drift and data drift. Next, you’ll discover various strategies for the continuous adaptation of models, including batch learning and incremental training techniques, to help your models evolve as new data arrives. Finally, you’ll explore model retraining frameworks, employing automated pipelines and feedback loops to integrate real-world insights into ongoing model adjustments. When you’re finished with this course, you’ll have the skills and knowledge of continuous training needed to keep your machine learning models at peak performance, adapting to new data.
Author Name: Amber Israelsen
Author Description:
Amber has been a software developer and technical trainer for over two decades, sharing her expertise in AI, machine learning, AWS, and Power Apps with students around the world. She has a knack for making complex tech topics easy to grasp, whether you’re a developer, designer, or business professional. Amber holds certifications in machine learning, AWS, and various Microsoft technologies, including her experience as a former Microsoft Certified Trainer. With a strong background in visual com… more

Table of Contents

• Course Overview
  1min
• Why Continuous Model Training?
  14mins
• Frameworks, Evaluation, and Feedback
  12mins