How to Benchmark Machine Learning Models

• 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

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:

1. Fundamentals of Benchmarking:

   • Understanding AI benchmarking and its significance.

   • Differences between NLP and CV benchmarks.

   • Key metrics for effective evaluation.

2. Setting Up Your Environment:

   • Installing tools and frameworks like Hugging Face, Python, and CIFAR-10 datasets.

   • Building reusable benchmarking pipelines.

3. Working with Datasets:

   • Utilizing popular datasets like CIFAR-10 for Computer Vision.

   • Preprocessing and preparing data for NLP tasks.

4. Model Performance Evaluation:

   • Comparing performance of various AI models.

   • Fine-tuning and evaluating results across benchmarks.

   • Interpreting scores for actionable insights.

5. Tooling for Benchmarking:

   • Leveraging Hugging Face and OpenAI GPT tools.

   • Python-based approaches to automate benchmarking tasks.

   • Utilizing real-world platforms to track performance.

6. Advanced Benchmarking Techniques:

   • Multi-modal benchmarks for NLP and CV tasks.

   • Hands-on tutorials for improving model generalization and accuracy.

7. 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