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Benchmarking, Improving AI Model - BLEU, TER, GLUE and more
Rating: 4.3 out of 5(6 ratings)
63 students

Benchmarking, Improving AI Model - BLEU, TER, GLUE and more

Master the art of benchmarking Machine learning models for any usage from Generative AI to narrow ai as computer vision
Last updated 6/2025
English

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
  • Benchmark RAG with RAGAs

Course content

16 sections83 lectures7h 7m total length
  • Introduction7:40
  • About your Instructor2:00
  • 5 minute AI Benchmark Challenge5:04

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:

  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.

  8. Benchmark RAG implementations

    1. RAGAS

    2. Coherence

    3. Confident AI - Deepeval

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