Tech Stack & Industry Tools
A practical toolkit of frameworks, platforms, and tools I use to build and operate production AI systems.
🤖 Core AI & ML Frameworks
PyTorch
Deep learning, LLMs, research-grade models. Industry standard for AI research and production.
TensorFlow
Production ML, inference engines, mobile deployment. Enterprise-ready AI framework.
Hugging Face
Pre-trained models, Transformers library. Gateway to modern NLP and generative AI.
LangChain
LLM applications, chains, agents, memory management. Build complex AI workflows.
LlamaIndex
RAG pipelines, data indexing, retrieval systems. Knowledge-grounded AI systems.
scikit-learn
Classical ML, preprocessing, evaluation metrics. Foundation for ML pipelines.
📊 Vector Databases & Data Infrastructure
Pinecone
Fully managed vector search, serverless. Production-ready vector database.
Weaviate
Open-source vector DB with hybrid search. Flexible, scalable architecture.
Chroma
Lightweight, embeddable vector store. Easy integration for applications.
Milvus
Scalable, high-performance vector search. Enterprise AI applications.
PostgreSQL pgvector
Open-source vector extension. Integrate vectors with SQL databases.
MongoDB Atlas
Vector search on documents. Native support in managed MongoDB.
🚀 MLOps & Deployment
MLflow
Experiment tracking, model registry, serving. Manage complete ML lifecycle.
Weights & Biases
ML experiment platform, sweeps, reports. Collaboration and reproducibility.
DVC
Data versioning, experiment management, pipelines. Git for ML projects.
Docker & Kubernetes
Containerization, orchestration, scaling. Production-ready deployments.
FastAPI
High-performance APIs for models, async support. Modern Python web framework.
BentoML
Model packaging, serving, deployment. Unified platform for model serving.
📈 Monitoring & Governance
Prometheus
Metrics collection & monitoring. Time-series database for observability.
Grafana
Dashboards & alerting, visualization. Beautiful monitoring dashboards.
Arize
ML model monitoring & observability. Detect drift and model degradation.
Evidently
Data drift & model performance tracking. Proactive model health monitoring.
Great Expectations
Data quality & validation, testing. Ensure data reliability throughout pipelines.
Fairness Toolkit
Bias detection & ethical AI, responsible ML. Build fair and transparent systems.
🔮 Emerging & Future Technologies
Staying ahead requires monitoring emerging technologies that will reshape AI in the coming years:
- Multimodal Models: Vision + Text + Audio integration
- Edge AI & TinyML: Models on IoT and mobile devices
- Federated Learning: Privacy-preserving distributed training
- Quantum ML: Quantum computing for specific ML tasks
- AutoML & Neural Architecture Search: Automated model design
- Model Distillation: Efficient deployment of small models
- Retrieval-Augmented Generation: Knowledge-grounded AI
- Parameter-Efficient Fine-Tuning: LoRA, adapters, prompt tuning
- AI Safety & Alignment: RLHF and constitutional AI
- Differential Privacy: Data protection in ML systems
⚡ Frontend & Development Tools
React.js
UI libraries, dashboards
Streamlit
Quick ML app prototyping
Gradio
ML model demos & sharing
Jupyter
Interactive development
Git & GitHub
Version control
VS Code
Development environment
Want to Explore This Stack?
If you're learning or evaluating tools, start with the Resources and Insights pages. If you spot an issue or have a question about a specific tool, feel free to reach out.