How to Benchmark Machine Learning Models

How to Benchmark Machine Learning Models
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Courses24h
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[h1]Free Download How to Benchmark Machine Learning Models[/h1]
Published: 12/2024
Created by: Dan Andrei Bucureanu
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 64 Lectures ( 5h 3m ) | Size: 3.14 GB

Master the art of benchmarking Machine learning models for any usage from Generative AI to narrow ai as computer vision
[h2]What you'll learn[/h2]
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
[h2]Requirements[/h2]
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
[h2]Description[/h2]
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
[h2]Who this course is for[/h2]
AI Engineers
AI Project Managers
ML Testers
AI Testers
Production Owners that work with AI
Homepage:

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

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