Pluralsight - Scalability and Performance Optimization for ML

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Free Download Pluralsight - Scalability and Performance Optimization for ML
Released 4/2025
By Yasir Khan
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 50m | Size: 113 MB

This course covers hyperparameter tuning, caching, load balancing, and leveraging pre-trained models to improve speed, reduce latency, and enhance scalability in production.
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.
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Scalability and Performance Optimization for ML
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 50m | 113 MB
Instructor: Yasir Khan​

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.

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