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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.

Beginner
50m
(0)

Created by Yasir Khan

Last Updated Apr 25, 2025

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  • Course

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.

Beginner
50m
(0)

Created by Yasir Khan

Last Updated Apr 25, 2025

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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.

Scalability and Performance Optimization for ML
Beginner
50m
(0)
Table of contents

About the author
Yasir Khan - Pluralsight course - Scalability and Performance Optimization for ML
Yasir Khan
27 courses 0.0 author rating 0 ratings

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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