Introduction

What is DLWΛY?

DLWΛY (typed DLWAY, short for "Deep Learning, Your Way") is a browser-based development environment for machine learning and data work. It combines a Monaco-powered code editor, a visual model builder, a data hub, a preparation and pipeline engine, dashboards, and deployment tooling in a single tab. There is nothing to install.

Key features

• Code Editor: Monaco-powered editor with Python and JavaScript runtimes, multi-file workspaces, and an integrated terminal
• Visual Model Builder: drag-and-drop neural network design that exports real TensorFlow.js or Keras code
• Data Hub: ingest CSV, Parquet, images, or entire folders, connect live databases, and pull datasets from Kaggle and Hugging Face
• PrepFlow: a visual data-preparation graph with more than seventy transforms
• Pipelines: repeatable, schedulable runs that chain data, preparation, training and reporting steps
• Dashboard: Tableau-style chart building plus live training metrics
• AI Copilot: an assistant that can read, write and run code inside your project

Browser-based execution

Python uses Pyodide (a full CPython build compiled to WebAssembly) by default, and local models train using TensorFlow.js with WebGL or WebGPU acceleration. You can explicitly attach a trusted Kaggle or Jupyter server for remote Python compute; remote runs send code—and, when enabled, changed project text files—to that server. JavaScript and TensorFlow.js remain in the browser, while Model Builder lets you choose browser TF.js or native TensorFlow/scikit-learn on the attached kernel.

Private by default

Datasets, code and trained models are stored on your own device. Nothing is uploaded unless you choose to import from a hub, connect a database, attach a remote kernel, or call an AI provider. See Privacy and storage.

Next steps

Continue to Quick Start to create your first project, or check the browser requirements.

New to the Studio? The Studio Tour maps every module, and the tutorials walk through each one step by step.