ML Tools & Resources

A curated collection of essential tools for ML development, from model visualization to production deployment.

Quick Navigation

• Model Visualization & Analysis
• Browser-Based ML Inference (Full Guide)
• Model Training & Optimization
• Integration & Deployment
• Learning Resources

🎨 Model Visualization & Analysis

Netron ⭐ RECOMMENDED

Essential tool for visualizing and analyzing neural network models. Supports 40+ formats including TensorFlow, PyTorch, ONNX, and more.

→ Read Full Guide → Open Netron App → GitHub Repository

TensorFlow Model Card Generator

Generate structured documentation for your ML models with model cards - essential for transparency and reproducibility.

Key Features:

• Create comprehensive model documentation
• Document training data, performance metrics, and limitations
• Export as JSON, markdown, or HTML
• Share model information with teams and stakeholders

→ Learn More

🧠 Browser-Based ML Inference

For a comprehensive comparison of browser-based ML frameworks, performance benchmarks, and implementation strategies:

→ Read Complete Browser-Based Inference Guide

Quick Framework Links

• TensorFlow.js - Docs GitHub
• ONNX Runtime Web - Official Site
• MediaPipe - Official Site

🔧 Model Training & Optimization

PyTorch

Deep learning framework for research and production. Industry standard for ML research and development.

Key Capabilities:

• Dynamic computation graphs
• GPU acceleration (CUDA, MPS for Apple Silicon)
• Easy model export to ONNX for browser deployment
• Extensive pre-trained model zoo
• Strong community ecosystem

→ Official Site

Hugging Face Transformers

State-of-the-art pre-trained models for NLP, computer vision, and multimodal tasks. Perfect for fine-tuning.

Relevant Models:

• Vision models (ViT, DeiT) for image understanding
• Audio models (Wav2Vec, Whisper) for audio processing
• Multimodal models (CLIP) for cross-modal understanding
• 15,000+ pre-trained models available

→ Official Site

TensorFlow Model Optimization Toolkit

Comprehensive tools for optimizing models for deployment on edge devices and browsers.

Optimization Techniques:

• Quantization: Convert FP32 → INT8 (4× smaller, 2-4× faster)
• Pruning: Remove unnecessary weights
• Knowledge Distillation: Compress models into smaller ones
• Clustering: Reduce unique weight values

🎯 Critical for Browser Deployment:

• Reduce LipNet model size from 50MB → 5-10MB for mobile
• Maintain accuracy while improving inference speed
• Enable real-time performance on iOS

→ Official Site

🚀 Integration & Deployment

Hugging Face Model Hub

Central repository for sharing and discovering ML models. Includes hosted inference APIs.

Features:

• 15,000+ pre-trained models for various tasks
• Free hosted inference endpoints
• Version control for models
• Community discussions and model cards
• Easy integration with Python/JavaScript libraries

→ Browse Models

Google Colab

Free Jupyter notebook environment with GPU access. Perfect for training and experimentation.

Benefits:

• Free GPU/TPU access (Tesla T4, P100, etc.)
• Pre-installed ML libraries (TensorFlow, PyTorch, etc.)
• Easy sharing and collaboration
• Integration with Google Drive

→ Official Site

Gradio & Streamlit

Quick ways to create web UIs for ML models without frontend development.

Gradio - For Model Demos:

• Create interactive model interfaces in minutes
• Share publicly with link
• Built-in component library

Streamlit - For Data Apps:

• Build interactive dashboards and tools
• Real-time updates
• Deploy to Streamlit Cloud free

→ Gradio → Streamlit

Docker & Model Serving

Containerize and deploy ML models at scale.

Tools:

• TensorFlow Serving: Production-scale serving of TensorFlow models
• KServe: Kubernetes-native model serving
• BentoML: Package and deploy ML models
• MLflow: Model tracking and serving

→ TensorFlow Serving

📚 Learning Resources

Fast.ai

Top-down approach to deep learning. Excellent for practical ML development without heavy theory.

→ Visit Fast.ai

Papers With Code

Research papers with code implementations. Great for finding state-of-the-art methods with working code.

→ Visit Papers With Code

Kaggle

ML competitions and datasets. Great for learning and benchmarking your models against others.

→ Visit Kaggle

Tool Reference Table

* Free tier available, Pro tier paid

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