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Course
- AI
Scalability and Performance Optimization for ML
This course covers hyperparameter tuning, caching, load balancing, and leveraging pre-trained models to improve speed, reduce latency, and enhance scalability in production.
What you'll learn
Learn to optimize and scale ML models for efficiency and performance. In this course, Scalability and Performance Optimization for ML, you'll learn to enhance ML model efficiency for production environments. First, you’ll explore hyperparameter tuning and model compression to optimize performance. Next, you’ll discover scaling techniques, including caching and load balancing, to handle high-demand workloads. Finally, you’ll learn how to leverage pre-trained models and improve training data for better accuracy. By the end of this course, you'll have the skills to scale ML models, reduce latency, and optimize resource utilization effectively.
Table of contents
About the author
Dr. Yasir Khan is a global tech consultant and 38Labs founder. He's passionate about digital transformation, data & AI, and regularly shares technology insights on Pluralsight.
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