Accessing Data for Machine Learning Models
This document describes different approaches to accessing and preparing data required for training machine learning models.
Notebooks
This section explains how to run and test code directly in Notebooks, making it easy to experiment, visualize, and prototype.
Model training
The model training section demonstrates how to build, track, and manage machine learning experiments in Python.
Working with LLMs in Notebooks
Getting started guide for integrating Large Language Models (LLMs) into your notebook workflows.
Working with Databases in Notebooks
This page explains how to integrate databases into your notebook workflows.