A fellow front-end engineer asked me for good starting points to learn AI and ML. That’s when I realized that the current stack of technologies a ML Engineer / Data Scientist needs to know is quite large. Some of the technologies I use on a daily basis include:

• Python: the underlying programming language
• JupyterLab: for fast experimentation
• Pandas: for small data processing
• Apache Spark / Beam: for big data processing
• ScikitLearn: for ML basics, data splitting, and metrics
• PyTorch: for neural networks
• Langchain: for LLM chain building
• Streamlit / Plotly: for debug interfaces
• Productionization: Docker, Kubernetes, Cloud platforms (AWS, GCP, Azure), Terraform, FastAPI

In addition, there are numerous other tools and technologies I use for logging, monitoring, alerting, cloud-specific services, dbt, various databases, BI tools, analytics, dependency management, Kafka, and more.

It can be overwhelming, especially considering that almost every day a new and improved tool or technology emerges.

Happy learning!