orms
- Google Colab (Free GPU for small models)
- Kaggle (Free notebooks + datasets)
- Hugging Face Spaces (Free AI model hosting)
Paid Cloud AI Services (For Scaling)
- AWS SageMaker (Managed ML training)
- Google Vertex AI (AutoML & custom models)
- Azure ML Studio (Enterprise AI workflows)
GPU Providers (For Training Large Models)
- Lambda Labs (Cheap cloud GPUs)
- RunPod (Pay-as-you-go GPU instances)
- Paperspace (High-performance cloud GPUs)
Data Collection & Processing Tools
Data Scraping & Collection
- BeautifulSoup (Web scraping)
- Scrapy (Large-scale data extraction)
- Twitter/Reddit API (Social media data)
Data Cleaning & Visualization
- Pandas (Data manipulation)
- NumPy (Numerical computing)
- Matplotlib/Seaborn (Data visualization)
- Tableau Public (Free data dashboards)
Model Training & Optimization
Automated Machine Learning (AutoML)
- AutoGluon (AutoML for tabular data)
- H2O.ai (Enterprise AutoML)
- Google AutoML (No-code AI training)
Hyperparameter Tuning
- Optuna (Optimize model performance)
- Weights & Biases (W&B) (Experiment tracking)
Edge AI (On-Device AI)
- TensorFlow Lite (Mobile & IoT AI)
- ONNX Runtime (Cross-platform AI deployment)
AI Deployment & APIs
Model Deployment Tools
- Flask/FastAPI (Python backend for AI models)
- Streamlit (Quick AI web apps)
- Gradio (Easy AI demo interfaces)
AI API Platforms
- Hugging Face Inference API (Pre-trained NLP models)
- Replicate (Run open-source AI in the cloud)
Hardware for AI Development
Best GPUs for AI Training
- NVIDIA RTX 4090 (Best for local LLMs)
- NVIDIA A100 (Cloud/server-grade AI)
- Apple M3 (for ML on Mac)
Free Alternatives (No GPU Needed)
- Use Google Colab (Free T4 GPU)
- Kaggle (Free TPUs for some models)
AI Learning Resources (Free in 2025)
- Courses:
- Fast.ai (Practical Deep Learning)
- Andrew Ng’s ML Course (Coursera)
- Books:
- “Hands-On Machine Learning with Scikit-Learn & TensorFlow” (Aurélien Géron)
- Communities:
- r/MachineLearning (Reddit)
- Hugging Face Discord
Final Checklist for AI Development
- Choose a framework (PyTorch/TensorFlow)
- Get a GPU (Cloud/Colab if no local GPU)
- Collect & clean data (Pandas, OpenCV)
- Train & optimize model (AutoML, Optuna)
- Deploy AI (FastAPI, Hugging Face Spaces)
What’s Next?
- Want to build a chatbot? → Use LangChain + OpenAI API
- Need image recognition? → OpenCV + YOLOv9
- Making an AI voice clone? → ElevenLabs or RVC
Let me know if you need a step-by-step guide on a specific AI project!