The Data Science and Machine Learning environments are designed for a variety of data science tools and machine learning libraries. These include environments for XGBoost, LightGBM, Scikit-learn, SciPy, Pandas, NumPy, Matplotlib, Seaborn, Numba, and CuPy. Each environment is equipped with the necessary tools for efficient data analysis, machine learning model training, and numerical computations.

Data Science and Machine Learning

American Data Science

American Data Science helps AI/ML Engineers and Data Scientists accelerate their notebook development workflow with pre-built GPU environments, compute, and AI agents.

Introduction

An AI-integrated environment for data science and development powered by Jupyter.

Alph Editor

Interact with Agent Alph to analyze data, generate code, and create files directly within the Alph Editor.

Chat

A quick guide to analyzing CSV data and creating interactive visualizations using Agent Alph and Streamlit

Data Analytics to Streamlit

We are working on comprehensive tutorials to help you get the most out of American Data Science.

Tutorials Coming Soon

Launch your notebook servers on GPUs with cloud providers like Lambda Labs and DataCrunch at no additional cost.

Compute

A powerful AI assistant that generates and runs code directly in your notebook servers

Easily share your applications and services straight from your notebook servers.

Port Forwarding

Use your API Key for authentication when using our API and SDKs.

Authentication

Get Environments

Get Compute

Get Servers

Create Server

Stop Server

Start Server

Delete Server

Get File

Upload File

Run Code

gpt-4o-mini

gpt-4o

gpt-4

claude-3-5-sonnet

claude-3-5-haiku

Open Source

Forum

API Reference

Dashboard

Support

Getting started with powerful notebook servers on CPUs or H100s with a click of the button.

American Data Science CLI provides commands to manage notebook servers, environments, and compute resources from your terminal.

Work with and generate multiple file types in the Alph Editor.

Overview

Create and edit Jupyter notebooks in the Alph Editor.

Notebooks

Generate and edit Python scripts in the Alph Editor.

Python

Work with structured data in JSON format in the Alph Editor.

JSON

Create rich documentation and reports in the Alph Editor.

Markdown

View and generate visual content in the Alph Editor.

Images

Open and analyze PDF documents in the Alph Editor.

A fully-featured terminal with persistent sessions and AI assistance.

Terminal

Run web applications directly from your server with secure browser access.

Visualize your data and code structure with automated knowledge extraction.

Graph Generator

Combine Alph Editor tools for a seamless development experience.

Integrating Tools in Your Workflow

Our environments are GPU-enabled and support PyTorch, Jax, RAPIDS, Transformers, and much more.

The AI environments are each tailored for specific AI tools and SDKs. They include tools for general AI development like PyTorch, as well as those specifically designed for working with AI models and tools from Anthropic, Cohere, Fireworks, Langchain, Mistral AI, OpenAI, Perplexity, and Replicate. Each environment provides the necessary clients, tools, and libraries for efficient development and interaction with the respective AI models and APIs.

The Deep Learning and Tensors environments are designed for specific deep learning libraries and tools. These include environments for PyTorch, JAX, TensorFlow, Hugging Face, and Fast.ai, each equipped with the necessary tools for efficient deep learning and tensor computations. They provide a streamlined experience for tasks such as training neural network models, high-performance numerical computing, and large-scale machine learning.

Environment	Description	Quickstart
datascience	Environment equipped with tools like XGBoost, LightGBM, Scikit-learn, and SciPy, Pandas, NumPy, Matplotlib, Seaborn, Numba, and CuPy.
rapidsai	Environment designed for RAPIDS.ai tools like cuDF, cuML, and cuGraph, all powered by NVIDIA GPUs.
cupy	Environment set up for CuPy, a GPU-accelerated library for numerical computations.
numba	Environment equipped with Numba, a just-in-time compiler for Python that helps developers accelerate scientific computing with GPUs.
scipy	Environment designed for SciPy, a Python library used for scientific and technical computing.
sklearn	Environment for Scikit-learn, a machine learning library in Python.
xgboost	Environment for XGBoost, a scalable and flexible gradient boosting library that is GPU-compatible.
lightgbm	Environment for LightGBM, a gradient boosting framework that uses tree-based learning algorithms. Supports parallel, distributed, and GPU learning.

Get Started

Alph Editor

Tutorials

Features

Data Science and Machine Learning