Master the art of benchmarking Machine learning models for any usage from Generative AI to narrow ai as computer vision

What you'll learn

What is Machine Learning benchmarking and how does it work
Standard Metrics used in AI ( Reliability, F1 Score, Recall)
Run a test through an API
How to run a benchmark against GLUE Metric
How to run a benchmark against BLUE Metric
MMLU (Massive Multitask Language Understanding) Benchmarking
TruthfulQA -Evaluation of Truthfulness in Language Models
Run Benchmark against SQuAD (Stanford Question Answering Dataset)
Understand the AI Model Lifecycle
Perplexity and Bias Benchmarking
Benchmark Against AI Fairness- Bias in Bios
Usage of HuggingFace models for benchmark and training
Computer Vision benchmark with CIFAR 10 dataset

Requirements

some python programming experience, you can also do without
basic understanding of AI Principles
Desire to learn the hottest skill on the market
5$ API Credits for OPEN AI - optional, you can use free models
VS Code, Postman, Python, Node

Description

This comprehensive course delves into the essential practices, tools, and datasets for AI model benchmarking. Designed for AI practitioners, researchers, and developers, this course provides hands-on experience and practical insights into evaluating and comparing model performance across tasks like Natural Language Processing (NLP) and Computer Vision.What You'll Learn:Fundamentals of Benchmarking:Understanding AI benchmarking and its significance.Differences between NLP and CV benchmarks.Key metrics for effective evaluation.Setting Up Your Environment:Installing tools and frameworks like Hugging Face, Python, and CIFAR-10 datasets.Building reusable benchmarking pipelines.Working with Datasets:Utilizing popular datasets like CIFAR-10 for Computer Vision.Preprocessing and preparing data for NLP tasks.Model Performance Evaluation:Comparing performance of various AI models.Fine-tuning and evaluating results across benchmarks.Interpreting scores for actionable insights.Tooling for Benchmarking:Leveraging Hugging Face and OpenAI GPT tools.Python-based approaches to automate benchmarking tasks.Utilizing real-world platforms to track performance.Advanced Benchmarking Techniques:Multi-modal benchmarks for NLP and CV tasks.Hands-on tutorials for improving model generalization and accuracy.Optimization and Deployment:Translating benchmarking results into practical AI solutions.Ensuring robustness, scalability, and fairness in AI models.Hands-On Modules:Implementing end-to-end benchmarking pipelines.Exploring CIFAR-10 for image recognition tasks.Comparing supervised, unsupervised, and fine-tuned model performance.Leveraging industry tools for state-of-the-art benchmarking

Who this course is for

AI Engineers
AI Project Managers
ML Testers
AI Testers
Production Owners that work with AI
Homepage:

https://www.udemy.com/course/how-to-benchmark-machine-learning-models/