My first dive into AI
A collection of posts that tell my learning experience in ML/AI with Python, JupiterLAB, and OpenAI
It's a buzzword — I get it — and we don't really do AI. We are AI consumers. Some people do AI and build models, while most of us are left with the daunting task of making sense of it and monetizing it.
Nevertheless, there are buzzwords that an engineer must have on their resume — just patches on the shoulder. Do we need Kafka? 80% of web projects don't, but 80% of the job posts require it. It's just a fact, and the same goes with AI.
Training Setup
Without further ado, here is the link to my training setup:
My Docker-based AI/ML Training Environment
Dive into Docker, JupyterLab, and machine learning with ‘learning-python’. Features Python 3.9, NodeJS, and PostgreSQL with pgvector. Start with make start at localhost:8888.
Docker-based AI/ML training environment for Python and NodeJS
Embeddings and all the Black Magic
The first AI concept that truly fascinates me is embeddings:
Embeddings and all the Black Magic
Explore AI with ‘learning-python’: Dive into embeddings using OpenAI. Index English docs, search with Italian queries. Set up your API key, add funds, and start generating embeddings.
