06 / Skills
The tools behindthe systems.
From model training and agentic workflows to production deployment and cloud infrastructure. Pick a technology to see the projects and roles that actually use it.
- 52
- Technologies
- 5
- Capability groups
- 23
- Services they power
01 / Capability map
What I work with,and where it shows up.
No percentage bars - just the groups I work in and the evidence behind each tool.
Data Science & Machine Learning / 18 technologies
02 / Services
What these skillsturn into.
Data Engineering
Explore- Python
- SQL
- dbt
- Airflow
- Snowflake
- BigQuery
- AWS
Data Analytics
Explore- SQL
- Python
- Pandas
- Power BI
- Looker
- Tableau
AI / ML
Explore- Python
- scikit-learn
- PyTorch
- TensorFlow
- XGBoost
- FastAPI
GenAI / RAG
Explore- LangChain
- LlamaIndex
- OpenAI
- Claude
- Pinecone
- FAISS
- FastAPI
Agentic AI
Explore- LangGraph
- MCP
- OpenAI
- Claude
- Groq
- LangSmith
Automation
Explore- n8n
- Make.com
- Zapier
- GoHighLevel
- Retell AI
- HubSpot
- APIs
LLMOps / MLOps
Explore- MLflow
- DVC
- LangSmith
- Evidently
- Docker
- GitHub Actions
- AWS
Cloud / Production
Explore- AWS
- GCP
- Azure
- Docker
- Kubernetes
- Terraform
- Next.js
- FastAPI
Put it to work
Need one of theseon your project?
Tell me what you’re building and which parts are unclear. I’ll say what I’d use, what I’d avoid, and why.