AI Tools & Frameworks
The ecosystem of libraries, frameworks, and platforms that power modern AI development.
Deep Learning Frameworks
PyTorch
The dominant framework for research. Dynamic computation graphs, pythonic API, excellent debugging. Used by Meta, OpenAI, and most research labs.
Best for: Research, prototyping, custom architectures
TensorFlow / JAX
TensorFlow: Production-ready, extensive ecosystem (TF Serving, TF Lite). JAX: Composable transformations for high-performance numerical computing.
Best for: Production deployment, mobile/edge, Google ecosystem
Pre-trained Models & APIs
Hugging Face
Hub for NLP models. Transformers library, easy fine-tuning, model sharing
OpenAI API
Provides GPT-4o and GPT-5 family models, the o-series for reasoning, DALL·E for image generation, Sora for audio/visual synthesis, and Whisper for speech-to-text. Strong multimodal and agent capabilities.
Anthropic Claude
Claude Sonnet and Claude 4.x families — emphasis on safety, long-context variants (100K+ to 1M+ depending on release) and strong code/assistant behaviors.
Google Vertex AI / Gemini
Gemini family (Pro/Ultra/Nano) — native multimodal models productized across Vertex AI and Google AI Studio with expanded context windows and strong multilingual performance.
Data & Experimentation
Weights & Biases
Experiment tracking, visualization, model versioning
MLflow
Open source ML lifecycle platform
DVC
Data version control, Git for datasets
Ray
Distributed computing, hyperparameter tuning
Specialized Libraries
Computer Vision
OpenCV, torchvision, Detectron2, MMDetection
NLP
spaCy, NLTK, Gensim, sentence-transformers
Reinforcement Learning
Stable Baselines3, RLlib, OpenAI Gym
Classical ML
scikit-learn, XGBoost, LightGBM, CatBoost
Cloud Platforms
AWS SageMaker
Full ML platform, notebooks to deployment
Google Cloud AI
Vertex AI, TPUs, AutoML
Azure ML
Enterprise ML, MLOps integration
Getting Started Recommendations
- Deep Learning: Start with PyTorch + Hugging Face
- Computer Vision: PyTorch + torchvision or TensorFlow + Keras
- NLP: Hugging Face Transformers + sentence-transformers
- Classical ML: scikit-learn + pandas for most tasks
- Production: Docker + Kubernetes + MLflow
- Experimentation: Jupyter + Weights & Biases
Key Takeaways
- PyTorch dominates research; TensorFlow strong in production
- Hugging Face is the hub for pre-trained models
- Cloud platforms offer managed ML services but can be costly
- Choose tools based on your specific needs and constraints