LLM Apps: Prototyping, Model Evaluation, and Improvements

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 Environment
Learn 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 Basics
Explore 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 Case
Discover 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 Community
Tap 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 Performance
Experiment with temperature settings, top-K and top-P sampling, and hyperparameter tuning to optimize LLM responses and efficiency.

Supercharge Your AI with Data-Driven Insights
Improve 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 Pro
Compare 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 Models
Go 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 Matrices
Master 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