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ML Workspace

Appears in 10 awesome lists

All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code).

Open github.comml-tooling/ml-workspace

Found in these lists

Awesome Big Data

Section: Machine Learning · All-in-one web-based IDE specialized for machine learning and data science.

ActiveScore 84

AWESOME DATA SCIENCE

Section: Miscellaneous Tools · All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a Docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code)

FreshScore 92

Awesome Deep Learning

Section: Tools · All-in-one web-based IDE for machine learning and data science.

SlowScore 59

Awesome Homelab

Section: Development · 🛠 All-in-one web-based IDE specialized for machine learning and data science.

FreshScore 84

Awesome Jupyter

Section: Runtimes/Frontends · Docker image that includes Jupyter(Lab) and various packages for data science/machine learning.

FreshScore 88

Awesome JupyterLab

Section: Other · All-in-one web IDE for machine learning and data science. Combines Jupyterlab, VS Code, Tensorflow, and many other tools/libraries into one Docker image.

ArchivedScore 49

Awesome Machine Learning

Section: Tools · All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code).

FreshScore 93

Awesome MLOps

Section: Machine Learning Platform · All-in-one web-based IDE specialized for machine learning and data science.

FreshScore 80

Awesome-Pytorch-list

Section: Pytorch elsewhere · All-in-one web IDE for machine learning and data science. Combines Jupyter, VS Code, PyTorch, and many other tools/libraries into one Docker image.

FreshScore 88

Awesome TensorFlow

Section: Tools/Utilities · All-in-one web IDE for machine learning and data science. Combines Tensorflow, Jupyter, VS Code, Tensorboard, and many other tools/libraries into one Docker image.

SlowScore 72

TensorFlow

How to use the Hexagon Delegate to speed up model inference on mobile and edge devices. Also see blog post Accelerating TensorFlow Lite on Qualcomm Hexagon DSPs.

In 23 listsDetails

Opik

Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…

In 16 listsDetails

Netron

Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).

In 15 listsDetails

Apache Airflow

"Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it…

In 13 listsDetails

Ray

A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. ray.io

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Gradio

Build and share delightful machine learning apps, all in Python. The de facto standard for creating interactive ML demos with automatic UI generation from function signatures. Powers thousands of Hugging Face Spaces.

In 11 listsDetails

Prefect

Workflow management system that makes it easy to take your data pipelines and add semantics like retries, logging, dynamic mapping, caching, failure notifications, and more.

In 11 listsDetails

MindsDB

MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.

In 11 listsDetails