Llm Apps: Prototyping, Model Evaluation, And Improvements

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Llm Apps: Prototyping, Model Evaluation, And Improvements
Published 3/2025
Created by Dan Andrei Bucureanu
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Expert | Genre: eLearning | Language: English | Duration: 69 Lectures ( 5h 53m ) | Size: 3.25 GB
Design, Test, and Benchmark LLM Apps: Fast Prototyping and Smart Evaluation for Optimal Performance



What you'll learn
Understand the tech landscape of LLM powered APPs
When to use GEN AI and when to use Weak AI
Setup the tools to integrate AI into your standard APP
Get the basics of ai in module Introduction to AI
Overview of Machine Learning Types
Data Lifecycle - how data evolves with your ML Model
Foundation Model lifecycle
Fine Tunning of models through data
Fine Tunning of models through prompt
Fine Tunning of models through hyperparameter
Using Huggingface models for work
Agentic Frameworks as: Autogen, Browser User, Flowise AI
Understand RAG and how to evaluate it
Evaluate the LLM With RAGAs benchmarking Framework
Understand the Confusion Matrix: accuracy, Recall, F1 score
GLUE Benchmarking Framework
Retrain and fine tune a computer Vision model
Requirements
Some AI Experience
Experience with Prompting
Some coding experience with Python
Laptop abele to run VS code and some python apps
LLM Api key
7-8 Hours and the will to improve
Description
Unlock the full potential of Large Language Models (LLMs) by understanding prototyping, model evaluation, and benchmarking. This hands-on course takes you through every stage of LLM development-from building and selecting models to fine-tuning, testing, and benchmarking them with industry-standard tools. Whether you're an AI beginner or a professional looking to enhance your expertise, this course provides the skills needed to create high-performing AI applications.What You'll Learn: Set Up Your AI Development EnvironmentLearn how to prepare a powerful AI workspace with Python, VS Code, NPM, and essential AI libraries, ensuring a seamless development experience.Understand AI & Machine Learning BasicsExplore key concepts in AI, Machine Learning, and Deep Learning, including supervised vs. unsupervised learning, model training phases, and how LLMs process and generate responses.Selecting the Right AI Model for Your Use CaseDiscover how to choose the best pre-trained AI models for NLP, vision, and multi-modal applications. Learn when to use classification, clustering, and regression models and understand model complexity, speed, and accuracy trade-offs. Harness the Power of Retrieval-Augmented Generation (RAG)Enhance your AI applications with RAG, a technique that combines retrieval-based search with LLM responses for more accurate and context-aware AI outputs.Leverage the Hugging Face AI CommunityTap into the Hugging Face ecosystem-explore model repositories, learn about tokenizers and transformers, and contribute to the open-source AI movement.Fine-Tune Models for Maximum PerformanceExperiment with temperature settings, top-K and top-P sampling, and hyperparameter tuning to optimize LLM responses and efficiency.Supercharge Your AI with Data-Driven InsightsImprove model accuracy with K-Fold Cross Validation, learn effective data-splitting techniques, and explore overfitting and underfitting detection methods.Benchmark Your AI Models Like a ProCompare your models against industry benchmarks like GLUE and Hugging Face Leaderboards. Learn how to evaluate NLP models using standard metrics and perform real-world GLUE benchmarking with Python.Evaluate Computer Vision AI ModelsGo beyond text-based models! Learn how to benchmark vision-based AI models using CIFAR-10 and interpret test results for advanced model evaluation. Understand Model Evaluation with Confusion MatricesMaster Confusion Matrix analysis to assess classification model performance. Learn how to interpret True Positives, False Positives, False Negatives, and True Negatives to optimize AI predictions and reduce errors.Who Should Take This Course? AI enthusiasts eager to dive into LLM prototyping and evaluation Developers looking to build and refine state-of-the-art AI models Data scientists who want to benchmark AI performance with confidence Anyone interested in understanding AI model evaluation techniques
Who this course is for
Any Software engineer
Developers
AI engineers
Project Managers
Product Owners
AI Testing Engineers
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