Udemy – Deploying LLMs: A Practical Guide to LLMOps in Production 2024-11

What you will learn in the course

• Understand the operations behind managing large language models for seamless deployment.
• Learn techniques to ensure the accuracy and reliability of your models before publishing.
• Optimize your models for speed and efficiency to meet production needs.
• Set up and use MLflow for effective model lifecycle tracking, versioning, and management.
• Implement ML-Ops strategies for continuous integration and deployment (CI/CD) of AI models.
• Learn how to safely integrate these methods into existing pipelines.
• Implement advanced classification, dynamic categories, and quantization to accelerate model inference.
• Exploring innovations such as Flash Attention, Paged Attention, GPTQ, AWQ, and LoRa to improve model performance.
• Use advanced scaling techniques such as ZeRO and DeepSpeed ​​to handle large deployments.
• Balance between performance and affordability for maximum return on investment.
• Understand the business implications of deployment decisions to make informed decisions.
• Mastery of techniques for deploying LLMs across clusters for high reliability and scalability.
• Learn how to effectively manage big data transfers.
• Implement frameworks to accelerate AI workloads across multiple devices.

This course is suitable for people who:

• Designed for AI professionals, data scientists, software engineers, and business professionals aiming to integrate AI into their operations, this course provides deep insight into deploying large language models with an emphasis on scalability, cost efficiency, and ML-Ops, making it valuable for any company looking to leverage AI for competitive advantage.

Installation Guide

After Extract, view with your favorite player.

Subtitles: English

Quality: 720p