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Home > Data > Artificial Intelligence > LLMs Under the Hood – Building Models for Your Unique Use Cases [Video]
LLMs Under the Hood – Building Models for Your Unique Use Cases [Video]
LLMs Under the Hood – Building Models for Your Unique Use Cases [Video]

LLMs Under the Hood – Building Models for Your Unique Use Cases: A comprehensive guide to building, optimizing, and deploying large language models [Video]

By Maxime Labonne , Denis Rothman , Abi Aryan
$79.99
Video Jan 2024 4 hours 2 minutes 1st Edition
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$79.99
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Key benefits

  • Led by 4 experts with extensive hands-on experience
  • Structured curriculum taking you from concepts to deployment
  • Comprehensive coverage of the complete LLM development lifecycle

Description

This in-depth masterclass provides end-to-end coverage of developing enterprise-grade LLMs tailored to your unique use cases. Led by experts Maxime Labonne, Dennis Rothman, and Abi Aryan, this video delivers the advanced skills needed to architect performant LLMs that deliver real business impact. You'll learn how to make crucial architecture decisions, select optimal model types, configure hyperparameters, and curate quality training data. Discover professional techniques for pre-training, iterative fine-tuning, and rigorous model evaluation. The instructors reveal insider strategies to productionize your LLMs smoothly, monitor them proactively, and maintain optimal performance post-deployment. Following a structured curriculum spanning the complete LLM lifecycle, this masterclass empowers you with hands-on skills to build, refine, and deploy large language models with confidence. Turbocharge your generative AI initiatives and get the practical knowledge needed to create LLMs that solve complex challenges for your organization.

What you will learn

How to select the right LLM architecture for your use case Approaches for sourcing, cleaning, and labeling quality training data Pre-training methods and hyperparameter optimization Advanced fine-tuning techniques to boost performance Ways to thoroughly evaluate LLMs before deployment Best practices for monitoring, updating, and maintaining production LLMs

Product Details

Country selected
Publication date : Jan 22, 2024
Length 4 hours 2 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781835883549
Category :
Data
Concepts :
Artificial Intelligence

What do you get with a video?

Product feature icon Download this video in MP4 format
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details

Publication date : Jan 22, 2024
Length 4 hours 2 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781835883549
Category :
Data
Concepts :
Artificial Intelligence

Table of Contents

1 Chapters
1. LLMs Under the Hood Chevron down icon Chevron up icon
Opening Keynote: Demystifying LLMs – Building, Engineering, and Deploying
Architecting LLMs - Text Generation, Code Interpretation, and More
Modeling LLMs: Input Data, Pre-Training, Fine-Tuning, and Model Evaluation
Productionizing LLMs - Prepare for Production

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About the 3 authors

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Profile icon Abi Aryan
ML Engineer and Author.
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Profile icon Mr. Denis Rothman
LinkedIn
Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive natural language processing (NLP) chatbots applied as an automated language teacher for Moët et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an advanced planning and scheduling (APS) solution used worldwide.
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Profile icon Mr. Maxime Labonne
LinkedIn Github
Maxime Labonne is currently a senior applied researcher at Airbus. He received a M.Sc. degree in computer science from INSA CVL, and a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris. During his career, he worked on computer networks and the problem of representation learning, which led him to explore graph neural networks. He applied this knowledge to various industrial projects, including intrusion detection, satellite communications, quantum networks, and AI-powered aircrafts. He is now an active graph neural network evangelist through Twitter and his personal blog.
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